A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
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
An evaluation of automated tumor detection techniques of brain magnetic reson...Salam Shah
Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
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
An evaluation of automated tumor detection techniques of brain magnetic reson...Salam Shah
Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
Recognition of brain cancer and cerebrospinal fluid due to the usage of diffe...journalBEEI
Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms & implementation with gives some predictions about the future generated by modified technology.
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.
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
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.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey on Segmentation Techniques Used For Brain Tumor DetectionEditor IJMTER
In recent years Brain tumor is one of the most commonly found causes for death among
children and adults. Early detection of tumor is a must in order to reduce the death rate. For tumor
detection various image techniques can be used. In this paper we mainly concentrate on the images
obtained from MRI scans. In MRI images, the tumor may appear clearly, but for further treatment
the physician need to be a qualified and well experienced person. In order to help the radiologist in
detection computer-aided diagnosis was developed. The generation of a CAD system consists of
several processes and among them segmentation is considered to the most important process. Image
Segmentation is a process of partitioning an image into multiple segments. The main objective of
segmentation is to represent the image into a simplified form so as to increase the efficiency and
accuracy of the system. Therefore the segmentation of brain tumor can be considered as an important
role in the medical image process. Hence in this paper we concentrate on the recently used
segmentation techniques for the detection of tumor using MRI images.
MRI Image Segmentation by Using DWT for Detection of Brain Tumorijtsrd
Brain tumor segmentation is one of the critical tasks in the medical image processing. Some early diagnosis of brain tumor helps in improving the treatment and also increases the survival rate of the patients. The manual segmentation for cancer diagnosis of brain tumor and generation of MRI images in clinical routine is difficult and time consuming. The aim of this research paper is to review of MRI based brain tumor segmentation methods for the treatment of cancer like diseases. The magnetic resonance imaging used for detection of tumor and diagnosis of tissue abnormalities. The computerized medical image segmentation helps the doctors in treatment in a simple way with fast decision making. The brain tumor segmentation assessed by computer based surgery, tumor growth, developing tumor growth models and treatment responses. This research focuses on the causes of brain tumor, brain tumor segmentation and its classification, MRI scanning process and different segmentation methodologies. Ishu Rana | Gargi Kalia | Preeti Sondhi ""MRI Image Segmentation by Using DWT for Detection of Brain Tumor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25116.pdf
Paper URL: https://www.ijtsrd.com/computer-science/bioinformatics/25116/mri-image-segmentation-by-using-dwt-for-detection-of-brain-tumor/ishu-rana
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
Air quality forecasting using convolutional neural networksBIJIAM Journal
Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deathseach year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from thedamage which is caused by air pollution is one of the major issues for the global community. The prediction ofair pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science tomaximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this researchis to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, whichincludes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposedsystem will be implemented in two steps; the first step will focus on data analysis and pre-processing, includingfiltering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters ofeach layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for thedeveloped CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a highaccuracy of 86.585%. The overall model is implemented using MATLAB software.
Prevention of fire and hunting in forests using machine learning for sustaina...BIJIAM Journal
Deforestation, illegal hunting, and forest fires are a few current issues that have an impact on the diversity andecosystem of forests. To increase the biodiversity of species and ecosystems, it becomes imperative to preservethe forest. The conventional techniques employed to prevent these issues are costly, less effective, and insecure.The current systems are unreliable and use more energy. By utilizing an Internet of Things (IOT) system, thistechnology offers a more practical and economical method of continuously maintaining and monitoring the statusof the forest. To guarantee excellent security, this system combines a number of sensors, alarms, cameras, lights,and microphones. It aids in reducing forest loss, animal trafficking, and forest fires. In the suggested system,sensors are used for monitoring, and cloud storage is used for data storage. Through the use of machine learning,the raspberry pi camera module significantly aids in the prevention of unlawful wildlife hunting as well as thedetection and prevention of forest fires.
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Recognition of brain cancer and cerebrospinal fluid due to the usage of diffe...journalBEEI
Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms & implementation with gives some predictions about the future generated by modified technology.
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.
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
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.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey on Segmentation Techniques Used For Brain Tumor DetectionEditor IJMTER
In recent years Brain tumor is one of the most commonly found causes for death among
children and adults. Early detection of tumor is a must in order to reduce the death rate. For tumor
detection various image techniques can be used. In this paper we mainly concentrate on the images
obtained from MRI scans. In MRI images, the tumor may appear clearly, but for further treatment
the physician need to be a qualified and well experienced person. In order to help the radiologist in
detection computer-aided diagnosis was developed. The generation of a CAD system consists of
several processes and among them segmentation is considered to the most important process. Image
Segmentation is a process of partitioning an image into multiple segments. The main objective of
segmentation is to represent the image into a simplified form so as to increase the efficiency and
accuracy of the system. Therefore the segmentation of brain tumor can be considered as an important
role in the medical image process. Hence in this paper we concentrate on the recently used
segmentation techniques for the detection of tumor using MRI images.
MRI Image Segmentation by Using DWT for Detection of Brain Tumorijtsrd
Brain tumor segmentation is one of the critical tasks in the medical image processing. Some early diagnosis of brain tumor helps in improving the treatment and also increases the survival rate of the patients. The manual segmentation for cancer diagnosis of brain tumor and generation of MRI images in clinical routine is difficult and time consuming. The aim of this research paper is to review of MRI based brain tumor segmentation methods for the treatment of cancer like diseases. The magnetic resonance imaging used for detection of tumor and diagnosis of tissue abnormalities. The computerized medical image segmentation helps the doctors in treatment in a simple way with fast decision making. The brain tumor segmentation assessed by computer based surgery, tumor growth, developing tumor growth models and treatment responses. This research focuses on the causes of brain tumor, brain tumor segmentation and its classification, MRI scanning process and different segmentation methodologies. Ishu Rana | Gargi Kalia | Preeti Sondhi ""MRI Image Segmentation by Using DWT for Detection of Brain Tumor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25116.pdf
Paper URL: https://www.ijtsrd.com/computer-science/bioinformatics/25116/mri-image-segmentation-by-using-dwt-for-detection-of-brain-tumor/ishu-rana
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
Air quality forecasting using convolutional neural networksBIJIAM Journal
Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deathseach year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from thedamage which is caused by air pollution is one of the major issues for the global community. The prediction ofair pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science tomaximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this researchis to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, whichincludes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposedsystem will be implemented in two steps; the first step will focus on data analysis and pre-processing, includingfiltering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters ofeach layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for thedeveloped CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a highaccuracy of 86.585%. The overall model is implemented using MATLAB software.
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Drug recommendation systems are systems that have the capability to recommend drugs. On a daily basis, a huge amount of data is being generated by the patients. All this valuable data can be properly utilized to create a reliable drug recommendation system. In this paper, we recommend a system for drug recommendations. The main scope of our system is to predict the correct medication based on reviews and ratings. Our proposed system uses natural language processing techniques (NLP), recurrent neural networks (RNN), and cellular automata (CA). We also considered various metrics like precision, recall, accuracy, F1 score, and ROC curve as measures of our system’s performance. NLP techniques are being used for gathering useful information from patient data, and RNN is a machine learning methodology that works really well in analyzing textual data. The system considers various patient data attributes like age, gender, dosage, medical history, and symptoms in order to make appropriate predictions. The proposed system has the potential to help medical professionals make informed drug recommendations.
Discrete clusters formulation through the exploitation of optimized k-modes a...BIJIAM Journal
This article focuses on the results of self-funded quantitative research conducted by social workers working inthe “refugee” crisis and social services in Greece (1). The research, among other findings, argues that front-lineprofessionals possess specific characteristics regarding their working profile. Statistical methods in the researchperformed significance tests to validate the initial hypotheses concerning the correlation between dataset variables.On the contrary of this concept, in this work, we present an alternative approach for validating initial hypothesesthrough the exploitation of clustering algorithms. Toward that goal, we evaluated several frequently used clusteringalgorithms regarding their efficiency in feature selection processes, and we finally propose a modified k-Modesalgorithm for efficient feature subset selection.
Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decom...BIJIAM Journal
This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Cl...BIJIAM Journal
Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.
Role of Artificial Intelligence in Supply Chain ManagementBIJIAM Journal
The term “supply chain” refers to a network of facilities that includes a variety of companies. To minimise the entire cost of the supply chain, these entities must collaborate. This research focuses on the use of Artificial Intelligence techniques in supply chain management. It includes supply chain management examples like as
demand forecasting, supply forecasting, text analytics, pricing panning, and more to help companies improve their processes, lower costs and risk, and boost revenue. It gives us a quick rundown of all the key principles of economics and how to comprehend and use them effectively.
An Internet – of – Things Enabled Smart Fire Fighting SystemBIJIAM Journal
Fire accident is a mishap that could be either man made or natural. Fire accident occurs frequently and can be controlled but may at time result in severe loss of life and property. Many a times fire fighters struggle to sort out the exact source of fire as it is continuously flammable and it spreads all over the area. On this concern, we have designed an Internet – of – Things (IoT) based device which sorts out the exact source of fire through a software and
hardware devices and also the complete detail of an area can be visualized by fire fighters which are preinstalled in the software itself. Because of our system’s intelligence in decision making during fire fighting, the proposed system is claimed as ‘Smart Fire Fighting System’. The implementation of the smart fire fighting system can make
the fire fighters to analyze the current situation immediately and make the decision quicker in an effective manner. Because of this system, fire losses in a building can be greatly reduced and many lives can be rescued immediately and moreover fire spread can also be restricted.
Evaluate Sustainability of Using Autonomous Vehicles for the Last-mile Delive...BIJIAM Journal
This study aims to determine if autonomous vehicles (AV) for last-mile deliveries are sustainable from three perspectives: social, environmental, and economic. Because of the relevance of AV application for last-mile delivery, safety was only addressed for the sake of societal sustainability. According to this study, it is rather safe to deploy AVs for delivery since current society’s speed restriction and decent road conditions give the foundation for AVs to run safely for last-mile delivery in metropolitan areas. Furthermore, AV has a distinct advantage in the event of a pandemic. The biggest worry for environmental sustainability is the emission problem. In terms of a series of emissions, it is established that AV has a substantial benefit in terms of emission reduction. This is mostly due to the differences in driving behaviour between autonomous cars and human vehicles. Because of AV’s commercial character, this research used a quantitative approach to highlight the economic sustainability of AV. The
study demonstrates the economic benefit of AV for various carrying capacities.
Automation Attendance Systems Approaches: A Practical ReviewBIJIAM Journal
Accounting for people is the first step of every manpower-based organization in today’s world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both
implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves
the path for researchers by reviewing practical methods and technologies used for existing attendance systems.
Transforming African Education Systems through the Application of IOTBIJIAM Journal
The project sought to provide a paradigm for improving African education systems through the use of the IOT. The created IOT model for Africa will enable African countries, notably Namibia, to exchange educational content and resources with other African countries. The objective behind the IOT paradigm in Africa’s education sectors is to provide open access to knowledge and information. The study revealed that there are no recognised platforms in African education systems that are utilised by African governments to interact, communicate, and share educational material directly with African institutions. As a result, the current research developed a model for
transforming African education systems using the IOT in the Namibian context, which will serve as a centralised online platform for self-study, new skill acquisition, and self-improvement using materials provided by African institutions of higher learning. Everyone is welcome to use the platform, including students, instructors, and
members of the general public.
Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the ...BIJIAM Journal
Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient’s neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, & laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep & is an important step in assisting physicians in the diagnosis & treatment of associated sleep disorders. In this research, we offer a unique method & a practical strategy to predicting early onsets of sleep disorders, such as restless leg syndrome & insomnia, using the Twin Convolutional Model FTC2, based on an algorithm composed of two modules. To provide localised time-frequency information, 30 second long epochs of EEG recordings are subjected to a Fast Fourier Transform, & a deep convolutional LSTM neural network is trained for sleep stage categorization. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a
daily basis. Thereby, a novel approach for sleep stage classification is proposed which combines the best of signal
processing & statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code
evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.
Robotic Mushroom Harvesting by Employing Probabilistic Road Map and Inverse K...BIJIAM Journal
A collision-free path to a destination position in a random farm is determined using a probabilistic roadmap (PRM) that can manage static and dynamic obstacles. The position of ripening mushrooms is a result of picture processing. A mushroom harvesting robot is explored that uses inverse kinematics (IK) at the target
position to compute the state of a robotic hand for grasping a ripening mushroom and plucking it. The DenavitHartenberg approach was used to create a kinematic model of a two-finger dexterous hand with three degrees of freedom for mushroom picking. Unlike prior experiments in mushroom harvesting, mushrooms are not planted in a grid or design, but are randomly scattered. At any point throughout the harvesting process, no human interaction is necessary
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
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• Remote control system for accessing CCR and allied system over serial or TCP.
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• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
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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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
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.
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.
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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/
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
1. BOHR International Journal of Internet of Things, Artificial Intelligence and Machine Learning
2022, Vol. 1, No. 1, pp. 79–84
https://doi.org/10.54646/bijiam.013
www.bohrpub.com
Enhanced 3D Brain Tumor Segmentation Using Assorted
Precision Training
Adwaitt Pandya1, Ozioma Collins Oguine2, Harita Bhargava1 and Shrikant Zade1
1Computer Science Department, Oriental Institute of Science and Technology, Bhopal, India
2Computer Science Department, University of Abuja, Nigeria
E-mail: adwaitt1999@gmail.com; oziomaoguine007@gmail.com; haritabhargava28@gmail.com;
cdzshrikant@gmail.com
Abstract. A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described
as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches,
seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant.
Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a
crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification
of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-
dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss
function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score
of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
Keywords: Brain tumor, 3D segmentation, brain tumor segmentation, 3D convolutional neural network, fully
convolutional neural network.
INTRODUCTION
A brain tumor is an abnormal growth of cells in the
brain that may or may not be cancerous. Tumors are
generally classified into two classes: benign and malignant.
Benign tumors are considered non-cancerous, i.e., they
grow locally and do not spread to other tissues. They can be
fatal if they develop near vital organs like the brain, even
after being non-cancerous. Malignant tumors are consid-
ered cancerous. New cells are constantly produced in our
body to replace the old ones; sometimes DNA gets dam-
aged during this renewal process, so the new cells develop
abnormally. These cells continue to multiply faster, thus
forming a tumor. Malignant tumors can spread and affect
other tissues. Tumors that affect the central nervous system
are known as gliomas. Following are the constituents of
gliomas:
Edema: Finger-like projection, an agglomerate of fluid or
water. FLAIR and T2-weighted sequences produce the best
results.
Necrosis: Collection of dead cells. Best seen in the T1 post-
contrast sequence.
Enhancing tumor: Indicates breakdown of the blood–brain
barrier. Seen in T1c post-contrast sequence.
Non-enhancing tumor: Seen in regions not included in
edema, necrosis, or enhancing tumor.
There are many reasons why a person can have a brain
tumor-like growth of cells uncontrollably in the brain due
to mutation or defect in a gene. This is a reason for causing
a brain tumor. Exposure to large amounts of X-rays is also
an environmental cause which leads to the development of
brain tumors. A tumor’s effects on your body are evaluated
by its size, location, and growth rate. General symptoms
include changes in the pattern of headaches, nausea or
vomiting, vision problems like blurred vision, tiredness,
speech and hearing difficulties, and memory problems.
Early diagnosis of a tumor provides the patient with
the best chance of successful treatment. There are fewer
chances of survival, a high cost of treatment, and many
more problems arise if the care is delayed.
79
2. 80 Adwaitt Pandya et al.
Early diagnosis improves the outcomes of treatment.
Diagnosing a brain tumor generally begins with a mag-
netic resonance imaging (MRI) scan. MRI is an imaging
technique that utilizes magnetic fields and radio waves
to create detailed images of the organs and tissues. MRI
can be used to measure the tumor’s size. For diagnosing
a tumor, the accuracy of conventional MRI is generally
satisfactory, but we should not rely heavily on it. One of the
important and efficient techniques for diagnosing tumors
is brain tumor segmentation. The technique of separating
tumors from other brain parts in an MRI scan of the brain
is called brain tumor segmentation. It separates tumors
from normal brain tissues. Tumor segmentation helps in
correctly identifying the spatial location of a tumor. Brain
tumor segmentation proves to be useful for diagnosis
and treatment planning. However, sometimes it is hard to
segment the tumor because of irregular boundaries in MRI
scans. If a tumor is detected on time due to segmentation,
it will prove to be very convenient from the doctor’s
perspective to commence treatment planning as soon as
possible.
Our dataset consists of a neuroimaging informatics tech-
nology initiative (NIFTI) file format. About 10 years ago,
the NIFTI file format was envisioned as a replacement
for the 7.5 file format for analysis. In image informatics
for neuroscience and even neuroradiology research, NIFTI
files are frequently employed. For our project, we used the
brain tumor segmentation (BraTS) dataset. The Radiolog-
ical Association of North America (RSNA), the American
Society of Neuroradiology (ASNR), and the Medical Image
Computing and Computer-Assisted Interventions (MIC-
CAI) society are working together to arrange the BraTS
challenge. The model used by us for segmentation is “Seg-
ResNet,” and we have trained it on the BraTS 2021 [10–14]
(Task 1) dataset. The following work contains a detailed
description of the dataset, proposed methodology, compar-
ative analysis, and results.
THEORETICAL BACKGROUND
Technology Stack
We used the Kaggle notebooks for training, validation,
and testing our model. By default, Kaggle provides P100
GPUs for all notebooks with 16 GB of RAM and 16 GB of
storage space. We used PyTorch version 1.10.0 and MONAI
0.7.0 for coding. We also used intensity normalization [9]
version 2.1.1 for normalizing intensities.
Dataset
We used the BraTS 2021 task-1 dataset. The collection
includes segmentation masks, Native (T1), T1-weighted
(T1Gd), T2-weighted (T2), and T2-Fluid Attenuated Inver-
sion Recovery (Recovery) NIFTI volumes for 1251 individ-
uals from various sources and in axial, sagittal, and coronal
orientation. All of the files were NIFTI volumes containing
240 image slices. Each image was 240 × 155 in size.
Literature Survey
For the purpose of segmenting brain tumors, Havaei et al.
proposed a CNN architecture that not only utilized local
and global features concurrently. They obtained dice scores
of 0.85 for segmenting the entire tumor, 0.78 for segmenting
the tumor core, and 0.73 for improving tumor segmenta-
tion [1].
Pereira et al. [2] explored a way to counter large spa-
tial and structural variability by incorporating small 3*3
kernels in their proposed architecture. They attained a
dice score of 0.88 on the whole tumor segmentation; for
tumor core segmentation, they were able to get a score of
0.83, and for enhancing tumor, they got 0.77. Myronenko
et al. [3] described an encoder-decoder-like architecture
and a variational autoencoder branch. Their model yielded
a dice score of 0.81, 0.90, and 0.86 on enhancing tumor,
whole tumor, and tumor core segmentation.
An anisotropic and dilated convolution filter-based cas-
cade of a fully convolutional neural network was proposed
by Wang et al. [4]. They broke the issue down into a series
of binary categorization issues. On the entire tumor, tumor
core, and improving tumor segmentation, their model
received scores of 0.78, 0.87, and 0.77, respectively.
Mohammadreza et al. [5] extracted text-on-descriptor
features such as histograms and first-order intensities,
which were then fed into a Random Forest Classifier. They
scored 0.84 on the whole tumor, 0.82 on the enhancing
tumor, and 0.78 on the tumor core segmentation.
Lyu et al. [6] used a 2-stage model; for stage 1, they
used an encoder-decoder-like architecture and variation
autoencoder regularization. In stage 2, the network uses
attention gates and is trained on a dataset formed by
stage 1 output. Their dice scores for the whole tumor,
tumor core, and enhancing tumor were 0.87, 0.83, and 0.82,
respectively.
METHODOLOGY
Preprocessing: The dataset comes from various sources.
We used the intensity normalization technique described
by Shinohara et al. [9] over every image of every modality
(FLAIR, T1w, T1wCE, and T2w) to solve the difference
in intensities. Separate normalizers were used for each
modality and were trained on the images belonging to their
respective modalities. Since the images were in different
orientations, we oriented all of them in the RAS orienta-
tion.
It is imperative to note that certain types of tumors
are best seen in different modalities, like edema, which is
best seen in T2-weighted sequences and FLAIR images.
Necrosis is best visible in the T1 post-contrast sequence,
3. Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training 81
Figure 1. (a) Two randomly selected images from the T1wCE modality. (b) Intensities of the two images before normalization.
(c) Intensities of the two images after normalization.
Figure 2. Augmented samples of different modalities. Each “image channel” corresponds to a different modality.
Figure 3. Brain tumor segmentation masks.
and an enhancing tumor is best seen in the T1c post-
contrast sequence [15].
Image Augmentations: We cropped the image, keeping
the region of interest of 224*224*144. We then randomly
flipped the images across all three axes and randomly
scaled and shifted the intensities. We used no augmenta-
tions for validation or testing.
Loss Functions and Metric: We used the dice loss func-
tion [17] and metric, a region-based loss function, to cal-
culate similarities. Mathematically, the dice coefficient can
be expressed as follows:
D =
2 ∑N
i pigi
∑N
i p2
i + ∑N
i g2
i
where D is the dice coefficient, p and g are pairs of cor-
responding pixel values [18]. For training, we added a
small constant to the denominator to tackle the scenarios
in which the denominator becomes less than 0.
Hyperparameters: We initialized the model with 16 filters,
keeping the number of input channels equal to 4 and
yielding an output of 3 channels, corresponding to the
three classes discussed earlier. We also applied a dropout
with a dropout rate of 0.2. Due to memory constraints,
we kept the batch size to 1. We used the Adam optimizer
for training with a learning rate of 0.0001. We applied
L2 regularization with a regularization coefficient set to
0.00001 and trained the model for 10 epochs.
Model Architecture: We used the SegResNet architecture
with the number of downsampling blocks in each layer
being 1, 2, 2, and 4, respectively. The number of upsam-
pling blocks in each layer is 1, 1, and 1, respectively.
Training: We trained our model for 10 epochs and saved
the best-performing model. We modified the traditional
4. 82 Adwaitt Pandya et al.
Figure 4. (a) Average loss (left) and average dice score (right) per epoch. (b) Dice score for tumor core (left), whole tumor (center), and
enhanced tumor (right).
Figure 5. The model’s output (top) is compared to the actual tumor (bottom). The yellow region is the enhanced tumor, the yellow and
green regions constitute the tumor core, and the yellow, red, and green regions constitute the whole tumor.
5. Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training 83
Table 1. Comparison of dice scores obtained by different methods.
Researches Number of Test Images Dice Scores
Havaei et al. [1] 200 2d slices and approximately 6000 2D images WT = 0.85, TC = 0.78, ET = 0.73
Pereira et al. [2] The training set contains 20 HGG and 10 LGG. WT = 0.88, TC = 0.83, ET = 0.77
Myronenko et al. [3] Training dataset included 285 cases (210 HGG and 75 LGG). ET = 0.81, WT = 0.90, CT = 0.86
Wang et al. [4] The training set contains images from 285 patients (210 HGG and 75
LGG).
WT = 0.7831, CT = 0.8739, ET = 0.7748
Jones et al. [5] The dataset was tested on 11 multimodal images and the BRATS 2013
clinical dataset using 30 multimodal images.
WT = 0.80, TC = 0.89
Lyu et al. [6] The BraTS 2020 dataset containing 259 HGG and 110 LGG cases ET = 0.79, WT = 0.90, TC = 0.83
Proposed 1251 NIFTI volumes of 240 image slices each ET = 0.71, WT = 0.90, TC = 0.84
training technique and used the mixed-precision [16] train-
ing method, which enhances performance and efficiency
and reduces memory requirements. We used automatic
mixed precision for both the training and validation tasks.
RESULT ANALYSIS
We converged our loss to 0.11 and got an average dice score
of 0.84 on the validation set. As for the separate classes, our
dice scores were as follows (on the validation set):
TC = 0.84
WT = 0.90
ET = 0.79
On the test set, we got a mean dice score of 0.86; in the
TC class, the dice score was 0.86; in the WT class, it was
0.92; and finally, on ET, it was 0.81.
DISCUSSION
Brain tumor segmentation proves to be an effective tool
for accurately diagnosing the tumor and its constituents.
Our model can segment a tumor from an MRI image
efficiently. The model was trained on 750 NIFTI volumes
and validated and tested on 250 NIFTI volumes. We were
able to get our loss down to 0.11 and got a mean dice
score of 0.84. We believe that more data could significantly
improve the model’s performance for future research.
CONCLUSION
In this study, we used the SegResNet architecture to seg-
ment the brain tumor. Our model produces great results
on 200 test cases. Our model’s best score produced a dice
score of 0.86 on TC, 0.92 on WT, and 0.81 on ET. In contrast,
the mean dice score was 0.84.
DISCLOSURE
The authors declare that they have no competing interests
with anyone in publishing this paper.
AUTHOR CONTRIBUTIONS
All authors made substantial contributions in conscripting
this manuscript and revising it critically for important
intellectual content, agreed to submit it to the current
journal, and approved the final version.
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