Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
Neural Network Based Classification and Diagnosis of Brain HemorrhagesWaqas Tariq
The classification and diagnosis of brain hemorrhages has work out into a great importance diligence in early detection of hemorrhages which reduce the death rates. The purpose of this research was to detect brain hemorrhages and classify them and provide the patient with correct diagnosis. A possible solution to this social problem is to utilize predictive techniques such as sparse component analysis, artificial neural networks to develop a method for detection and classification. In this study we considered a perceptron based feed forward neural network for early detection of hemorrhages. This paper attempts to spot on consider and talk about Computer Aided Diagnosis (CAD) that chiefly necessitated in clinical diagnosis without human act. This paper introduces a Region Severance Algorithm (RSA) for detection and location of hemorrhages and an algorithm for finding threshold band. In this paper different data sets (CT images) are taken from various machines and the results obtained by applying our algorithm and those results were compared with domain expert. Further researches were challenged to originate different models in study of hemorrhages caused by hyper tension or by existing tumor in the brain.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
Neural Network Based Classification and Diagnosis of Brain HemorrhagesWaqas Tariq
The classification and diagnosis of brain hemorrhages has work out into a great importance diligence in early detection of hemorrhages which reduce the death rates. The purpose of this research was to detect brain hemorrhages and classify them and provide the patient with correct diagnosis. A possible solution to this social problem is to utilize predictive techniques such as sparse component analysis, artificial neural networks to develop a method for detection and classification. In this study we considered a perceptron based feed forward neural network for early detection of hemorrhages. This paper attempts to spot on consider and talk about Computer Aided Diagnosis (CAD) that chiefly necessitated in clinical diagnosis without human act. This paper introduces a Region Severance Algorithm (RSA) for detection and location of hemorrhages and an algorithm for finding threshold band. In this paper different data sets (CT images) are taken from various machines and the results obtained by applying our algorithm and those results were compared with domain expert. Further researches were challenged to originate different models in study of hemorrhages caused by hyper tension or by existing tumor in the brain.
Design AI platform using fuzzy logic technique to diagnose kidney diseasesTELKOMNIKA JOURNAL
Artificial intelligence (AI) is an advanced scientific technology that can provide strong ability to assist in analysis and diagnosis of almost every type of data, therefore; AI widely used in medical fields, which is applied in the diagnosis and early detection of diseases. Kidney disease is one of the common diseases that are diagnosed and the necessary treatments are suggested by artificial intelligence. In this research, a logic system was used. The fuzzy logic system (FLS) is one of the artificial intelligence systems for diagnosing kidney diseases, where the fuzzy logic system divided into five variable inputs, namely urea, creatinine, glucose, bun, and uric acid, and they represented laboratory tests of the patients, this variables and also three outputs were identified, which are chronic inflammation and kidney failure, stones and salts, acute inflammation of the kidneys and bladder, which is the result of the medical diagnosis of the disease. Five memberships for inputs and three memberships for outputs are used in FLS. Diseases are concluded based on the values of the inputs, and thus the system proved its effectiveness and accuracy in diagnosis and this system is considered an aid to the specialized doctors in the field of kidney diseases.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
New methodology to detect the effects of emotions on different biometrics in...IJECEIAES
Recently, some problems have appeared among medical workers during the diagnosis of some diseases due to human errors or the lack of sufficient information for the diagnosis. In medical diagnosis, doctors always resort to separating human emotions and their impact on vital parameters. In this paper, a methodology is presented to measure vital parameters more accurately while studying the effect of different human emotions on vital signs. Two designs were implemented based on the microcontroller and National Instruments (NI) myRIO. Measurements of four different vital parameters are measured and recorded in real time. At the same time, the effects of different emotions on those vital parameters are recorded and stored for use in analysis and early diagnosis. The results proved that the proposed methodology can contribute to the prediction and diagnosis of the initial symptoms of some diseases such as the seventh nerve and Parkinson’s disease. The two proposed designs are compared with the reference device (beurer) results. The design using NI myRIO achieved more accurate results and a response time of 1.4 seconds for real-time measurements compared to its counterpart based on microcontrollers, which qualifies it to work in intensive care units.
The last several decades have seen cardiovascular illnesses become the leading cause of mortality globally, in both industrialized and developing nations alike. Clinical staff monitoring and early diagnosis of heart disorders can both lower death rates. However, because it takes more intelligence, time, and skill, precise cardiac disease identification in every case and 24-h patient consultation by a doctor are not yet possible. With the use of machine learning techniques, a preliminary concept for a cloud-based system to predict heart disease has been put out in this study. An effective machine-learning strategy should be applied for the precise identification of cardiac illness. This method was created after a thorough comparison of many machine learning methods in MATLAB coding. The application may thus be utilized by the medical professionals to monitor the patient’s real-time sensor data and begin live video streaming if urgent care is necessary. The ability of the suggested method to notify both parties right away when the patient checks the stage while the doctor isn’t there was a crucial component.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Artificial neural network for cervical abnormalities detection on computed to...IAESIJAI
Cervical cancer is the second deadliest after breast cancer in Indonesia.
Sundry diagnostic imaging modalities had been used to decide the location
and severity of cervical cancer, one among those is computed tomography
(CT) Scan. This study handles a CT image dataset consisting of two
categories, abnormal cervical images of cervical cancer patients and normal
cervix images of patients with other diseases. It focuses on the ability of
segmentation and classification programs to localize cervical cancer areas
and classify images into normal and abnormal categories based on the
features contained in them. We conferred a novel methodology for the
contour detection round the cervical organ classified with artificial neural
network (ANN) which was employed to categorize the image data. The
segmentation algorithm used was a region-based snake model. The texture
features of the cervical image area were arranged in the form of gray level
co-occurrence matrix (GLCM). Support vector machine (SVM) had been
added to determine which algorithm was better for comparison.
Experimental results show that ANN model has better receiver operating
characteristic (ROC) parameter values than SVM model’s and existing
approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and
95.75% of accuracy.
Alzheimer’s detection through neuro imaging and subsequent fusion for clinica...IJECEIAES
In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Slantlet transform used for faults diagnosis in robot armIJEECSIAES
The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same purpose.
A comparative study for the assessment of Ikonos satellite image-fusion techn...IJEECSIAES
Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (PanchromaticPAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images: MULTIWavelet 2D-M, MULTIG-S, MULTIMHF, MULTIHPF, MULTISMV, and MULTIPCA. In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the MULTISMV image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.
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Design AI platform using fuzzy logic technique to diagnose kidney diseasesTELKOMNIKA JOURNAL
Artificial intelligence (AI) is an advanced scientific technology that can provide strong ability to assist in analysis and diagnosis of almost every type of data, therefore; AI widely used in medical fields, which is applied in the diagnosis and early detection of diseases. Kidney disease is one of the common diseases that are diagnosed and the necessary treatments are suggested by artificial intelligence. In this research, a logic system was used. The fuzzy logic system (FLS) is one of the artificial intelligence systems for diagnosing kidney diseases, where the fuzzy logic system divided into five variable inputs, namely urea, creatinine, glucose, bun, and uric acid, and they represented laboratory tests of the patients, this variables and also three outputs were identified, which are chronic inflammation and kidney failure, stones and salts, acute inflammation of the kidneys and bladder, which is the result of the medical diagnosis of the disease. Five memberships for inputs and three memberships for outputs are used in FLS. Diseases are concluded based on the values of the inputs, and thus the system proved its effectiveness and accuracy in diagnosis and this system is considered an aid to the specialized doctors in the field of kidney diseases.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
New methodology to detect the effects of emotions on different biometrics in...IJECEIAES
Recently, some problems have appeared among medical workers during the diagnosis of some diseases due to human errors or the lack of sufficient information for the diagnosis. In medical diagnosis, doctors always resort to separating human emotions and their impact on vital parameters. In this paper, a methodology is presented to measure vital parameters more accurately while studying the effect of different human emotions on vital signs. Two designs were implemented based on the microcontroller and National Instruments (NI) myRIO. Measurements of four different vital parameters are measured and recorded in real time. At the same time, the effects of different emotions on those vital parameters are recorded and stored for use in analysis and early diagnosis. The results proved that the proposed methodology can contribute to the prediction and diagnosis of the initial symptoms of some diseases such as the seventh nerve and Parkinson’s disease. The two proposed designs are compared with the reference device (beurer) results. The design using NI myRIO achieved more accurate results and a response time of 1.4 seconds for real-time measurements compared to its counterpart based on microcontrollers, which qualifies it to work in intensive care units.
The last several decades have seen cardiovascular illnesses become the leading cause of mortality globally, in both industrialized and developing nations alike. Clinical staff monitoring and early diagnosis of heart disorders can both lower death rates. However, because it takes more intelligence, time, and skill, precise cardiac disease identification in every case and 24-h patient consultation by a doctor are not yet possible. With the use of machine learning techniques, a preliminary concept for a cloud-based system to predict heart disease has been put out in this study. An effective machine-learning strategy should be applied for the precise identification of cardiac illness. This method was created after a thorough comparison of many machine learning methods in MATLAB coding. The application may thus be utilized by the medical professionals to monitor the patient’s real-time sensor data and begin live video streaming if urgent care is necessary. The ability of the suggested method to notify both parties right away when the patient checks the stage while the doctor isn’t there was a crucial component.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Artificial neural network for cervical abnormalities detection on computed to...IAESIJAI
Cervical cancer is the second deadliest after breast cancer in Indonesia.
Sundry diagnostic imaging modalities had been used to decide the location
and severity of cervical cancer, one among those is computed tomography
(CT) Scan. This study handles a CT image dataset consisting of two
categories, abnormal cervical images of cervical cancer patients and normal
cervix images of patients with other diseases. It focuses on the ability of
segmentation and classification programs to localize cervical cancer areas
and classify images into normal and abnormal categories based on the
features contained in them. We conferred a novel methodology for the
contour detection round the cervical organ classified with artificial neural
network (ANN) which was employed to categorize the image data. The
segmentation algorithm used was a region-based snake model. The texture
features of the cervical image area were arranged in the form of gray level
co-occurrence matrix (GLCM). Support vector machine (SVM) had been
added to determine which algorithm was better for comparison.
Experimental results show that ANN model has better receiver operating
characteristic (ROC) parameter values than SVM model’s and existing
approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and
95.75% of accuracy.
Alzheimer’s detection through neuro imaging and subsequent fusion for clinica...IJECEIAES
In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Slantlet transform used for faults diagnosis in robot armIJEECSIAES
The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same purpose.
A comparative study for the assessment of Ikonos satellite image-fusion techn...IJEECSIAES
Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (PanchromaticPAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images: MULTIWavelet 2D-M, MULTIG-S, MULTIMHF, MULTIHPF, MULTISMV, and MULTIPCA. In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the MULTISMV image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.
Elliptical curve cryptography image encryption scheme with aid of optimizatio...IJEECSIAES
Image encryption enables users to safely transmit digital photographs via a wireless medium while maintaining enhanced anonymity and validity. Numerous studies are being conducted to strengthen picture encryption systems. Elliptical curve cryptography (ECC) is an effective tool for safely transferring images and recovering them at the receiver end in asymmetric cryptosystems. This method's key generation generates a public and private key pair that is used to encrypt and decrypt a picture. They use a public key to encrypt the picture before sending it to the intended user. When the receiver receives the image, they use their private key to decrypt it. This paper proposes an ECC-dependent image encryption scheme utilizing an enhancement strategy based on the gravitational search algorithm (GSA) algorithm. The private key generation step of the ECC system uses a GSAbased optimization process to boost the efficiency of picture encryption. The image's output is used as a health attribute in the optimization phase, such as the peak signal to noise ratio (PSNR) value, which demonstrates the efficacy of the proposed approach. As comparison to the ECC method, it has been discovered that the suggested encryption scheme offers better optimal PSNR values.
Design secure multi-level communication system based on duffing chaotic map a...IJEECSIAES
Cryptography and steganography are among the most important sciences that have been properly used to keep confidential data from potential spies and hackers. They can be used separately or together. Encryption involves the basic principle of instantaneous conversion of valuable information into a specific form that unauthorized persons will not understand to decrypt it. While steganography is the science of embedding confidential data inside a cover, in a way that cannot be recognized or seen by the human eye. This paper presents a high-resolution chaotic approach applied to images that hide information. A more secure and reliable system is designed to properly include confidential data transmitted through transmission channels. This is done by working the use of encryption and steganography together. This work proposed a new method that achieves a very high level of hidden information based on non-uniform systems by generating a random index vector (RIV) for hidden data within least significant bit (LSB) image pixels. This method prevents the reduction of image quality. The simulation results also show that the peak signal to noise ratio (PSNR) is up to 74.87 dB and the mean square error (MSE) values is up to 0.0828, which sufficiently indicates the effectiveness of the proposed algorithm.
A new function of stereo matching algorithm based on hybrid convolutional neu...IJEECSIAES
This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.
A YOLO and convolutional neural network for the detection and classification ...IJEECSIAES
The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computeraided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).
Development of smart machine for sorting of deceased onionsIJEECSIAES
Today, we are thinking to raise farmer’s income through various means and measures. Implementation of new crop patterns, technology inclusion and promoting the eshtablishment of numerous agro processing industries will play a major role in agriculture sector. The labour issue is also one of the main concerns in many of the agricultural activities. In this paper we propose a technological evolvement in onion detection process, where we apply image processing and sensory mechanism to identify sprouted and rotten onions respectively. This will yield to quick, accurate and prompt supply of goods to the market, irrespective of lack of consistent but costly manpower. The efficiency of this prototype in identifying the sprouted onions with the help of camera is observed to be upto 87% and also the response of Gas sensing system in detecting rooten onions under prescribed chamber dimensions is analysed and obtained encouraging results.
Efficient resampling features and convolution neural network model for image ...IJEECSIAES
The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.
State and fault estimation based on fuzzy observer for a class of Takagi-Suge...IJEECSIAES
Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...IJEECSIAES
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
Agricultural harvesting using integrated robot systemIJEECSIAES
In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.
Characterization of silicon tunnel field effect transistor based on charge pl...IJEECSIAES
The aim of the proposed paper is an analytical model and realization of the characteristics for tunnel field-effect transistor (TFET) based on charge plasma (CP). One of the most applications of the TFET device which operates based on CP technique is the biosensor. CP-TFET is to be used as an effective device to detect the uncharged molecules of the bio-sample solution. Charge plasma is one of some techniques that recently invited to induce charge carriers inside the devices. In this proposed paper we use a high work function in the source (ϕ=5.93 eV) to induce hole charges and we use a lower work function in drain (ϕ=3.90 eV) to induce electron charges. Many electrical characterizations in this paper are considered to study the performance of this device like a current drain (ID) versus voltage gate (Vgs), ION/IOFF ratio, threshold voltage (VT) transconductance (gm), and subthreshold swing (SS). The signification of this paper comes into view enhancement the performance of the device. Results show that high dielectric (K=12), oxide thickness (Tox=1 nm), channel length (Lch=42 nm), and higher work function for the gate (ϕ=4.5 eV) tend to best charge plasma silicon tunnel field-effect transistor characterization.
A new smart approach of an efficient energy consumption management by using a...IJEECSIAES
Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (Ap) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (Ap) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
Parameter selection in data-driven fault detection and diagnosis of the air c...IJEECSIAES
Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.
Electrical load forecasting through long short term memoryIJEECSIAES
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a
large amount of electrical energy cannot be stored. For the proper
functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis,
yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM)
neural networks, a recurrent neural network capable of handling both longterm and short-term dependencies of data sets, for predicting load that is to
be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
A simple faulted phase-based fault distance estimation algorithm for a loop d...IJEECSIAES
This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can
get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phasebased filtered signals can be obtained by using phase-modal and discrete
wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail
level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the
faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR)
for the nearest lines from the measured terminal.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
An internet of things-based automatic brain tumor detection system
1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 25, No. 1, January 2022, pp. 214~222
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v25.i1.pp214-222 214
Journal homepage: http://ijeecs.iaescore.com
An internet of things-based automatic brain tumor detection
system
Md. Lizur Rahman, Ahmed Wasif Reza, Shaiful Islam Shabuj
Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
Article Info ABSTRACT
Article history:
Received Apr 14, 2021
Revised Nov 18, 2021
Accepted Nov 26, 2021
Due to the advances in information and communication technologies, the
usage of the internet of things (IoT) has reached an evolutionary process in
the development of the modern health care environment. In the recent human
health care analysis system, the amount of brain tumor patients has increased
severely and placed in the 10th position of the leading cause of death.
Previous state-of-the-art techniques based on magnetic resonance imaging
(MRI) faces challenges in brain tumor detection as it requires accurate image
segmentation. A wide variety of algorithms were developed earlier to
classify MRI images which are computationally very complex and
expensive. In this paper, a cost-effective stochastic method for the automatic
detection of brain tumors using the IoT is proposed. The proposed system
uses the physical activities of the brain to detect brain tumors. To track the
daily brain activities, a portable wrist band named Mi Band 2, temperature,
and blood pressure monitoring sensors embedded with Arduino-Uno are
used and the system achieved an accuracy of 99.3%. Experimental results
show the effectiveness of the designed method in detecting brain tumors
automatically and produce better accuracy in comparison to previous
approaches.
Keywords:
Brain tumor
Healthcare
Internet of things
Magnetic resonance imaging
Wrist wearables
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ahmed Wasif Reza
Department of Computer Science and Engineering, East West University
Dhaka, Bangladesh
Email: wasif@ewubd.edu
1. INTRODUCTION
Nowadays, the network of physical objects embedded with electronics devices, sensors, and
software known as the internet of things (IoT) is becoming popular around the world. IoT has achieved
greater value and service by trading information and data with the manufacturer, operator, and/or other
connected devices. In today’s modern healthcare, IoT has attracted much attention recently for its potential to
ease the management and interoperability of patient-related and device information. Wearable devices like
wristwatches, bracelets, and rings are now widely used for remote healthcare to monitor the physiological
parameters of patients. In recent years, researchers have attracted a lot in this field to address the potential of
IoT in the healthcare system. In the medical environment, the brain tumor has now become a dangerous
problem and placed in 10th position as the leading cause of death [1], [2]. It is estimated that approximately
700,000 people are living with a brain tumor in America, among them 80% are benign and 20% are
malignant [3]. In recent years, approximately 78,980 adults are diagnosed with a brain tumor, among them
55,150 are benign and 23,830 are malignant [3]. It is also estimated that approximately 16,700 adults will die
from a brain tumor, among them 9,620 are male and 7,080 are female [4]. Also, about 4,830 children 0-19
years of age will be diagnosed with a brain tumor. According to the annual data in the United States, about
34% of men and 36% of females survive at least 5-years with brain tumors.
2. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
An internet of things -based automatic brain tumor detection system (Md. Lizur Rahman)
215
The IoT and information technologies now revolutionized the development of the healthcare system
which has reduced the risk and cost of monitoring and evaluating patients’ health conditions. Many
researchers presented different methods for the segmentation and detection of brain tumors. A model for
individualizing the texture of tumors in brain magnetic resonance imaging (MRIs) are proposed in [5]. To
testify the accuracy of their proposed technique, they used more than 300 MRIs of 14 patients and compare
their automatic segmentation of brain tumors using the MRIs technique to other segmentation of brain tumor
works. Using the K-means algorithm and normalized histogram segmentation technique, a brain tumor
detection technique is developed in [6]. They used the support vector machine (SVM) and Naïve Bayes
classifier for the classification and accuracy of their method. Authors in [7] suggest an automatic detection
and segmentation of brain tumors through the conditional random field in MRI images and obtained 89%
accuracy on average. A modified mean-shift-based fuzzy c-mean segmentation technique for the detection of
brain tumors is proposed in [8] which is fast to provide segmentation results. In [9], the authors proposed an
SVM and rough K-means-based brain tumor detection algorithm, which classify MRI images and claimed
almost 99.05% of accuracy. Authors in [10] convert MRI images into OtsoBinarization followed by K-means
clustering segmentation in brain tumor detection and classification. They used the discrete wavelet transform
technique to extract the features and SVM for classification of high accuracy. A better technique than
artificial neural network (ANN) and SVM techniques is proposed in [11] which includes k-means clustering
segmentation, high concentration slurry disposal (HCSD) method, extraction of features, and k-nearest
neighbors (KNN) classifier. Authors in [12] proposed IoT based malignant tumor prediction system, where
they used only three physical symptoms and their accuracy is not good. A hybrid feature extraction method
was used based on discrete wavelet transform and principal component analysis to identify the brain tumor
[13]. Based on the compression of MRI brain images an automatic tumor region extraction system was
proposed in [14]. Principle component analysis and ANN techniques were used to detect and recognize the
brain tumor [15], but that system used only 20 MRI images for training purposes and 45 MRI images for
testing purposes.
In this paper, an IoT-based automatic brain tumor detection system is designed and developed.
Different symptoms of brain tumors are classified to extract their internal characteristics and measured by
using sensors. Portable wristband Mi Band 2, temperature, and blood pressure monitoring sensors are used in
the experiments to monitor the different symptoms of patients from time to time. Patients’ information is
stored in a developed mobile application via a third-party server. A comparison study has been made between
sick and healthy people based on their extracted physiology information to testify the effectiveness of the
developed brain tumor detection system.
The paper is organized as follows. In section 2, the methodology of the proposed brain tumor
detection technique is discussed. The classifications and measurement of different symptoms related to brain
tumors are explained in this section. The experimental data collection and data transfer techniques are
discussed in section 3. Section 4 shows the result and discussion part of this system including the extracted
experimental dataset. Section 5 shows the accuracy and comparison part of the system while section 6
provides the conclusion.
2. PROPOSED METHOD
For detection of brain tumor, seven common symptoms including- headache, vomiting or nausea,
vision change, seizures, walking problem (consider normal people who can walk), drowsiness or sleeping
problems are fatigue considered. Firstly, we will classify those symptoms and corresponding information.
Then we will sense that information using sensors.
2.1. Symptoms analysis
Since there is no wearable sensor for capturing all the symptoms data correctly, we use classification
in those symptoms. Based on the classification information, we will collect data by using our proposed
device. The classifications of symptoms are shown in Table 1.
2.2. Measurements of classification symptoms
For every classification symptom (CS), a defined value is set to compare with the observed value.
For example, 140/90 is the defined value of blood pressure. To measure the CS, in (1) is proposed which can
be stated.
𝐶𝑆 𝑣𝑎𝑙𝑢𝑒 = {
1, 𝑜𝑏𝑠𝑒𝑟𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 ≥ ℎ𝑖𝑔ℎ 𝑑𝑒𝑓𝑖𝑛𝑒𝑑 𝑣𝑎𝑙𝑢𝑒
1, 𝑜𝑏𝑠𝑒𝑟𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 < 𝑙𝑜𝑤 𝑑𝑒𝑓𝑖𝑛𝑒𝑑 𝑣𝑎𝑙𝑢𝑒
−1, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(1)
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In (1), observe value is the sensor’s sensed value, and the high defined value (HDV) is such kind of
symptoms values that will be always greater than or equal to the defined value if a person has that symptom.
Similarly, a low defined value (LDV) is such a kind of symptoms value that will be always less than the
defined value if a person has that symptom. In this research, HDVs are high blood pressure, increased body
temperature, high heart rate, and a large amount of awake time in between sleep. Similarly, LDVs are a low
heart rate, a fewer number of steps, lower deep sleep, and insomnia.
Blood pressure has two values- systolic and diastolic. Table 2 shows the chart of blood pressure. If
the measured blood pressure becomes higher than HVD, the computer science (CS) value becomes equal to 1
according to (1), otherwise, CS becomes -1. Table 3 shows the normal body temperature for people aged 3 or
above. Body temperature 98° Fahrenheit is considered as the HDV for ‘increased body temperature’
symptoms [16]. On the other hand, HDV for high heart rate is considered as 101, and LDV for low heart rate
is chosen as 60 as shown in Table 4.
Table 1. Classification of brain tumor symptoms
Symptoms (SS) Classification Symptoms (CS) Time
Headache (HA) o High Blood pressure
o Increase body temperature
o Accompanied by vomiting
o Usually, steady pain after waking in the
morning
o Get better within a few hours
Vomiting or Nausea
(VN)
o Increase body temperature
o High heart rate
o High blood pressure
o Maybe occur in the morning
o Or when change the position
Vision changes (VC) o Low heart rate
o High blood pressure
o Headache
o Nausea and vomiting
o After waking from sleep
o Double or triple vision in one eye
o Suddenly change posture
Seizures (SZ) o Increasing heart rate (High heart rate)
o Increasing blood pressure (High blood pressure)
o Any time
o Blood pressure, heart rate get normal after
30 minutes of seizures
Walking problem (WP) o Less number of steps (compare to normal)
o Lack coordination in the arms or legs
o Any time of the day, face difficulties
to walk
Drowsiness or sleeping
problem (DS)
o Insomnia (less sleep than normal people)
o Less amount of deep sleep
o Falling asleep during the day
o Sometimes not sleeping until 5 or 6 am
Fatigue (FG) o Difficulty sleeping (Insomnia)
o Headache
o A large amount of Awake time in between Sleep
o Vision changes
o Whole day patient experiences this
symptom
Table 2. Blood pressure chart
Blood Pressure Systolic (top number) Diastolic (bottom number)
High Blood Pressure Systolic ≥ 140 Diastolic ≥ 90
Normal Blood Pressure 90 ≤ systolic < 140 60 ≤ systolic < 90
Low Blood Pressure Systolic < 90 Diastolic < 60
Table 3. Body normal temperature
Age Fahrenheit Celsius
3 to 5 years 98.6 to 99.0 37.0 to 37.2
7 to 9 years 98.1 to 98.3 36.7 to 36.8
Age ≥ 10 years 97.8 36.6
Table 4. Heart rate and average sleep chart
Age Heart Rate Average Sleep (Hours)
0 to 2 months 120 to 160 12 to 18
3 months to 1 year 80 to 140 14 to 15
1 to 3 years 80 to 130 12 to 14
3 to 5 years 80 to 120 11 to 13
6 to 12 years 70 to 110 10 to 11
Age ≥ 13 years 60 to 110 8.5 to 10
During insomnia known as sleeplessness, people may experience daytime sleepiness, a low level of
energy, and always get depressed [17]-[19]. Table 4 shows the hours of average sleep required for some
particular age groups. During insomnia, people may experience less sleep than normal people. Therefore, the
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LDV for ‘insomnia’ is considered as 4 hours or 240 min. If anyone sleeps less than this LDV value for a long
time, then he/she may have insomnia. The sleep stages for adults are given in Table 5 where the amount of
deep sleep is observed to be as around 50 to 60 minutes. Therefore, for the CS “Less amount of deep sleep,”
the LVD is set at 40 minutes. As observed, the average awake time for adults is 25 minutes. Therefore, the
HDV is considered as 35 minutes for the CS “Large amount of Awake time in between Sleep”.
Table 5. Sleep stages for adults
Sleep Stages Minutes
Light sleep 252 to 324
REM sleep 84 to 108
Deep sleep 50 to 65
Awake 25
For detecting the CS “Less number of steps”, its previous data are used as LDV and compared to the
present data as observe value and extract the result. Similar steps are also followed to find“ lack coordination
in the arm or legs” which is presented in (2).
𝐿𝑎𝑐𝑘 𝑐𝑜𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑡ℎ𝑒 𝑎𝑟𝑚𝑠 𝑎𝑛𝑑 𝑙𝑒𝑔𝑠 =
𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑡𝑒𝑝𝑠 𝑝𝑒𝑟 𝑑𝑎𝑦
𝑡𝑜𝑡𝑎𝑙 𝑎𝑐𝑡𝑣𝑖𝑡𝑦 𝑡𝑖𝑚𝑒 𝑖𝑛 𝑚𝑖𝑛𝑢𝑡𝑒𝑠
(2)
Around 30 minutes of average awake time in between sleep is considered normal. But if the awake
time in between sleep is greater than or equal to 35 minutes, it will be considered HDV for a large amount of
awake time in between sleep. Table 6 shows the HDV and LDV values for CS.
Table 6. HDV and LDV values for CS
CS HDV LDV
High Blood Pressure 140 & 90 -
Increase Body Temperature 98 -
High Heart rate 101 -
Low Heart Rate - 60
Insomnia - 240
Less amount of Deep Sleep - 40
Less number of Steps - Depending on previous data
Lack of Coordination in the arm or legs - Depending on previous data
A large amount of Awake time in between Sleep 35 -
2.3. Measurements of symptoms
For measuring symptoms (SS) mentioned in Table 1, (3) is proposed based on the related CS values.
𝑆𝑆 𝑣𝑎𝑙𝑢𝑒 = {
1, ∑ 𝐶𝑆 𝑣𝑎𝑙𝑢𝑒 ≥ 0
𝑝
1
−1, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(3)
In (3), ‘p’ is the total CS related to SS. Using (3), it is possible to identify the symptom of a brain
tumor of any random person or patient. For example, vomiting or nausea (VN) related CSs are increased
body temperature, high heart rate, and high blood pressure whose CS values could be either 1 or -1 according
to (1). If the summation related three CS’s value of VN symptoms is greater than or equal to zero then SS
becomes equal to 1 (means the selected person has VN symptoms), otherwise becomes -1.
2.4. Brain tumor prediction
After predicting all the SS values, the detection of a brain tumor can be done by using the proposed (4).
𝑃𝑜𝑠𝑖𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑏𝑟𝑎𝑖𝑛 𝑡𝑢𝑚𝑜𝑟 =
1
1+𝑒−𝐿 (4)
In (4), L is the sum of all SS values (HA+VN+VC+SZ+WP+DS+FG). The proposed (4) will show
the probability of brain tumors between 0 and 1. The percentage of this probability of brain tumor is divided
into a different class to make the decision shown in Table 7.
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Table 7. Decision table of brain tumor
Percentage probability of brain tumor Decision
70% or above Brain Tumor
30% ≤ percentage probability <70% Brain Tumor Candidate
Below 30% Normal
3. EXPERIMENTAL SETUP
3.1. Data collection from sensors
In the experiment, Xiaomi “Mi Band 2” wrist band as shown in Figure 1 is used which includes an
accelerometer, optical heart rate monitor, vibration engine, gyroscope, ambient light, and altimeter sensors
[20], [21]. The pedometer of MI Band 2 used an improved algorithm to measure steps more accurately. The
high-precision accelerometer measures the number of steps and tracks the total activity time for a total
number of steps. This device measures the heart rate by using an optical heart rate monitor sensor and tracks
deep sleep records. This device tracks the sleep pattern (deep and light sleep) of human and awake time in
between sleep by using a heart rate sleep assistant, which measures the heart rate when a human is asleep.
Using this wearable wristband, most of the CS symptoms can be measured. Another two individual sensors
are used to get the body temperature and blood pressure. All these sensors and the wristband are associated
with Arduino Uno to get the results. The wristband connects with the android smartphone using the Mi Fit
app to collect data from devices as shown in Figure 2(a). Mi Fit stores those data and shows the average
statistics and other information in terms of time (e.g., daily, weekly and monthly). Also, it can measure steps,
distances, and different physical activities. Figure 2(b) shows the block diagram of the data transmission
from sensors.
(a)
(b)
Figure 1. CS symptoms measured by MI Band 2 Figure 2. Data transmission through, (a) Mi band to Mi
Fit app and (b) sensors to android app
3.2. Data transfer and store in the server
An indirect access technique where an intermediate system works to collect data from the source to
the third party is used to transfer data from smartphone to server. Figure 2 illustrates the data flow of
wearable bands and sensors via indirect access. As shown, wearable MI Band 2 captures data clockwise and
sends those captured data to smartphones using the Mi Fit app termed as Send data 1. Arduino Uno transfers
the captured data from sensors through the global system for mobile communication (GSM) module to
android phones. This data sending process is termed here as Send data 2. Finally, the stored data in these
smartphones are transferred to the third-party server.
4. EXPERIMENTAL RESULTS
4.1. Datasets collections
The experimental data were extracted from two groups of people: one is from brain tumor patients
and the other is from normal people. Brain tumor patient data were collected from a renowned hospital in
Bangladesh through the clinical trial method. Total 375 brain tumor patient data are collected and used in this
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system. Normal people’s data were collected from university and college students, and staff. Total 625
normal person data were collected and used in this system for validation. In this paper, randomly selected 10
data from each group are used. Tables 8 and 9 show the data of the brain tumor patient and the normal person
used in this paper, respectively.
Table 8. Experimental data of brain tumor patient
Sample
info
Blood Pressure Body
Temperature
Heart
Rate
Total
Sleep
Total
Deep
Sleep
Total
Awake
Time
Total
Steps
Total
Activity
Time of
Steps
Previous
Total
Steps
Previous
Total
Activity
Time of
Steps
# Age
Systolic
mmhg
Diastolic
mmhg
F bpm min min min min min
1 41 162 94 100.1 51 230 38 34 5646 73 5644 75
2 47 156 90 99.1 55 249 51 66 6586 82 5739 80
3 52 140 89 98.5 101 198 31 59 5611 78 4235 56
4 56 145 96 99 108 237 42 30 4983 64 5415 71
5 49 138 110 99.2 102 241 43 53 5967 68 5613 64
6 53 133 92 97.8 101 217 36 47 4017 61 4768 66
7 44 157 101 98.5 112 234 39 32 4511 57 5241 65
8 31 152 104 99.2 57 298 31 47 3554 45 3922 47
9 42 143 96 98.7 52 211 43 64 5546 65 4132 56
10 39 148 98 98.7 96 179 29 71 4658 70 3219 63
Table 9. Experimental data of normal person
Sample
info
Blood Pressure Body
Temperature
Heart
Rate
Total
Sleep
Total
Deep
Sleep
Total
Awake
Time
Total
Steps
Total
Activity
Time of
Steps
Previous
Total
Steps
Previous
Total
Activity
Time of
Steps
# Age
Systolic
mmhg
Diastolic
mmhg
F bpm min min min min min
1 23 108 72 97.8 45 291 57 12 8723 101 8313 99
2 23 138 92 98.1 98 350 105 2 5993 66 5098 59
3 57 125 87 98 47 274 33 3 9329 97 10213 109
4 23 136 90 97.9 93 475 114 5 7921 88 8111 96
5 23 126 81 97.9 77 463 108 20 11310 112 12512 117
6 51 130 89 97.9 100 363 62 12 3527 43 3409 42
7 23 121 83 97.8 97 475 119 4 3120 39 3411 41
8 24 107 72 97.9 89 393 132 2 2739 36 2711 36
9 23 123 81 97.9 95 465 127 12 1857 28 1927 31
10 46 118 86 97.9 101 367 97 1 4614 55 4973 56
4.2. Experimental analysis
The measured data are analyzed in two scenarios to detect brain tumors. The experimental data of
brain tumor patients given in Table 8 are considered as scenario-1 and the data of the normal person as
shown in Table 9, are considered as scenario-2. The required CS symptoms are measured by using (1). These
measured values of CS are used to find the SS values via (3) shown in Table 10. Later, in (4) is used to
calculate the probability of brain tumor based on the measured SS values which are graphically depicted in
Figure 3. As observed, the designed system gives almost 99% of the probability of brain tumor except for
sample number 6 having the probability of 73.11%, who may not experience all the mentioned symptoms.
Table 10. Measured SS value for scenario-1
Sample info HA VN VC SZ WP DS FG Sum All (L)
# age
1 41 1 1 1 1 -1 1 1 5
2 47 1 1 1 1 -1 -1 1 3
3 52 1 1 1 1 1 1 1 7
4 56 1 1 1 1 1 1 1 7
5 49 1 1 1 1 -1 -1 1 3
6 53 -1 -1 -1 1 1 1 1 1
7 44 1 1 1 1 1 1 1 7
8 31 1 1 1 1 1 1 1 7
9 42 1 1 1 1 -1 1 1 5
10 39 1 1 1 1 -1 1 1 5
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In the case of scenario-2, data were collected from a normal person who regularly check-up their
health and does not contain a brain tumor. Table 11 shows the corresponding experimental CS and SS values
for scenario-2 which are measured in a similar way as for scenario-1. As shown in the case of the normal
person dataset in Figure 3, the system provides nearly zero probability of brain tumor except the sample
numbers 3 and 10 who may have walking and seizure problems that do not reflect the brain tumor symptoms.
Table 11. Measured SS value for scenario-2
Sample info HA VN VC SZ WP DS FG Sum All (L)
# age
1 23 -1 -1 -1 -1 -1 -1 -1 -7
2 23 -1 -1 -1 -1 -1 -1 -1 -7
3 57 -1 -1 -1 -1 1 1 -1 -3
4 23 -1 -1 -1 -1 1 -1 -1 -5
5 23 -1 -1 -1 -1 1 -1 -1 -5
6 51 -1 -1 -1 -1 -1 -1 -1 -7
7 23 -1 -1 -1 -1 1 -1 -1 -5
8 24 -1 -1 -1 -1 -1 -1 -1 -7
9 23 -1 -1 -1 -1 1 -1 -1 -5
10 46 -1 -1 -1 1 1 -1 -1 -3
5. RESULT ANALYSIS AND DISCUSSION
In our experiment, we divide our dataset into two scenarios and use 10 randomly taken samples for
each scenario. For each scenario, our proposed methodology performs very efficiently. We have used the
most popular and common metrics available to evaluate the results. We have also measured the accuracy
using the following equation.
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 𝑆𝑐𝑜𝑟𝑒 =
𝑇𝑃+𝑇𝑁
𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
(5)
where TP = True Positive (actually positive and predicted positive), FP = false positive (actually negative but
predicted positive), TN = true negative (actually negative and predicted negative), and FN = false negative
(actually positive but predicted negative). The accuracy of the designed system with the proposed model is
given in Table 12 for both scenarios 1 and 2. Figure 3 shows the graphical representation of our results.
Various techniques have been proposed to detect brain tumors. Most of them are image
classification-based. To find the efficiency of our method, we have compared our accuracy with other state-
of-the-art methodologies. Table 13 shows the accuracy table for various techniques.
Table 12. Accuracy of the proposed method for both scenarios
Dataset # of sample Identify correctly Accuracy score
Scenario-1 (Brain Tumor patient’s data) 375 375 100%
Scenario-2 (Normal person’s data) 625 618 98.88%
Total (both scenarios) 1,000 976 99.3 %
Figure 3. Probability of brain tumor for brain tumor patient’s dataset and normal people’s dataset
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Table 13. A comparison study of the proposed method with other existing techniques
System Accuracy
Naïve Bayes Classifier [6] 87.23%
Rao et al. [7] 89%
Dandil et al. [22] 90.79%
SVM classifier [6] 91.49%
Devasena and Hemalatha [23] 98.8%
El-Dahshan et al. [24] 99%
Halder and Dobe [9] 99.05%
Arakeri and Reddy [25] 99.09%
Proposed Method 99.3%
6. CONCLUSION
In this paper, a stochastic method for the automatic detection of brain tumors based on IoT is
proposed. A portable wristband device, and temperature and blood pressure sensors are used to track the
daily activities of both brain tumor patients and normal people. Different symptoms of brain tumors are
analyzed and classified as the selected common symptoms. The experimental dataset for both the brain tumor
patient and normal people groups testify to the accuracy of the proposed method for automatic detection of
brain tumors using IoT. This system achieved an accuracy of 99.3%. Compared to other existing techniques,
the designed system shows a better precision in detecting the probability of brain tumors and does not require
MRI which reduces the computational complexity. Moreover, the proposed portable system is cost-effective
and easy to use in comparison with other systems. Although the proposed system is easy to use and cost-
effective, our proposed system cannot detect the position and size of brain tumors. In the future, we will add
more functions to our system such that it can detect the position and size of the tumor as well.
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BIOGRAPHIES OF AUTHORS
Md. Lizur Rahman received the B.Sc. degree in computer science and
engineering from the East West University, Bangladesh, from 2014 to 2018. He has received
Summa Cum Laude Award from East West University. He is currently the leader of the
software department of a software company named Ultra-X BD Ltd. in Dhaka, Bangladesh.
He has authored or coauthored more than 15 publications: 4 proceedings and 11 journals, with
4 H-index and more than 70 citations. His research areas are the internet of things, deep
learning, universal networking language, natural language processing, and intelligent systems.
He can be contacted at email: lizur.sky@gmail.com.
Ahmed Wasif Reza obtained B.Sc (Hons.) in Computer Science and Engineering
from Khulna University (Bangladesh), Master of Engineering Science (M.Eng.Sc.) from
Multimedia University (Malaysia), and Doctor of Philosophy (Ph.D.) from University of
Malaya (Malaysia). Since August 2016, he is working as an Associate Professor at the
Department of Computer Science and Engineering (CSE), East West University, Bangladesh.
He was also appointed as the Chairperson of the CSE department. Previously, he was attached
with the University of Malaya, Department of Electrical Engineering, Faculty of Engineering,
Malaysia for almost 8 years. He is serving as a member of the Evaluation Team (ET) for
Accreditation of different programs of various universities, appointed by the Board of
Accreditation for Engineering and Technical Education (BAETE), Bangladesh. He also has
vast experience in supervising Ph.D., Masters, and Undergraduate students. He has been
placed in the “World Scientist and University Rankings 2021” ranked by “AD Scientific
Index”. He has been working in the field of radio frequency identification (RFID), wireless
communications, biomedical image processing, bioinformatics, data science, the internet of
things, machine learning, and deep learning. He has authored and co-authored several journals
and conference papers (about 130 papers; h-index: 21; citations: 1739). He can be contacted at
email: wasif@ewubd.edu.
Shaiful Islam Shabuj completed his B.Sc. degree in computer science and
engineering from the East West University, Bangladesh, from 2014 to 2018. He is currently
working as a software engineer on Ultra-X Asia Pacific Ltd. in Tokyo, Japan. His research
interest includes machine learning, the internet of things, natural language processing,
artificial intelligence, and pattern learning. He can be contacted at email:
shaifulshabuj@gmail.com.