This document presents research on using deep learning and image processing techniques for oral cancer detection. It proposes a deep convolutional neural network (DCNN) model with two branches - one for cancer detection and one for segmentation and region of interest marking. Texture maps extracted from input images are used as additional computer vision features for the DCNN model. The model is trained on labeled medical images and evaluated for its ability to automatically detect cancerous regions. Overall, the research aims to develop an accurate and automated system for oral cancer screening using deep learning and computer vision approaches.
IRJET- Classification of Cancer of the Lungs using ANN and SVM AlgorithmsIRJET Journal
1) The document compares the performance of artificial neural network (ANN) and support vector machine (SVM) classifiers in classifying lung cancer images.
2) Lung cancer is difficult to diagnose early due to subtle symptoms, and accurate classification of images can help doctors detect it earlier to improve survival rates.
3) The study evaluates the ANN and SVM classifiers on online cancer datasets using metrics like accuracy, sensitivity, specificity, and calculates true/false positives and negatives. The goal is to determine which classifier performs better for lung cancer image classification.
Breast Cancer Detection through Deep Learning: A ReviewIRJET Journal
Three sentences:
Convolutional neural networks show promise for automating breast cancer detection from medical images to address increasing caseloads. Previous research has developed CNN approaches for segmenting breast tissue and detecting cancer from mammograms and ultrasound images with strong accuracy. This literature review evaluates past works applying deep learning to breast cancer detection and segmentation tasks to inform the development of a new CNN-based approach detailed in future work.
IRJET- Intelligent Prediction of Lung Cancer Via MRI Images using Morphologic...IRJET Journal
The document describes a proposed system to intelligently predict lung cancer using MRI images and morphological neural network analysis. The proposed system uses a three-stage approach: preprocessing MRI images, extracting features using wavelet decomposition and normalization, and classifying tissues as normal or abnormal using a morphological neural network with image pruning. This combination of morphological image processing and neural networks is intended to more efficiently classify cancer cells and identify affected regions than previous methods.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
Shap Analysis Based Gastric Cancer DetectionIRJET Journal
This document proposes a novel deep learning framework to detect gastric cancer from endoscopic images of the stomach. The framework uses a patch-based analysis where features are extracted from image patches and evaluated for cancer risk. A bag-of-features technique is then applied to the extracted features from selected patches for analysis. Experimental results show the proposed framework can effectively and efficiently detect gastric cancer from images and identify minute lesions. It achieves higher accuracy than other models using the same dataset. The framework is also more accurate than existing methods for gastric cancer detection.
This document summarizes imaging techniques for colorectal cancer, including double-contrast barium enema, colonoscopy, and CT colonography (virtual colonoscopy). It discusses the benefits and limitations of each technique. For CT colonography, it reviews literature on its accuracy in detecting polyps and cancers in screening populations. Key factors that influence CT colonography results like bowel preparation, insufflation method, and image viewing technique are also summarized. The document concludes that further standardization of the CT colonography technique and additional large clinical trials are needed to establish it as a screening tool.
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
PNEUMONIA DIAGNOSIS USING CHEST X-RAY IMAGES AND CNNIRJET Journal
This document discusses using convolutional neural networks to diagnose pneumonia from chest x-ray images. Specifically, it summarizes several research papers that used CNN models like InceptionV3 to extract features from x-ray images and then trained classification algorithms like support vector machines, neural networks, and K-nearest neighbors to classify images as pneumonia or normal. The neural network model achieved 84.1% sensitivity while support vector machines obtained the highest AUC of 93.1%. In general, CNNs can accurately diagnose pneumonia from x-rays but training the models requires a large dataset and computing resources.
IRJET- Classification of Cancer of the Lungs using ANN and SVM AlgorithmsIRJET Journal
1) The document compares the performance of artificial neural network (ANN) and support vector machine (SVM) classifiers in classifying lung cancer images.
2) Lung cancer is difficult to diagnose early due to subtle symptoms, and accurate classification of images can help doctors detect it earlier to improve survival rates.
3) The study evaluates the ANN and SVM classifiers on online cancer datasets using metrics like accuracy, sensitivity, specificity, and calculates true/false positives and negatives. The goal is to determine which classifier performs better for lung cancer image classification.
Breast Cancer Detection through Deep Learning: A ReviewIRJET Journal
Three sentences:
Convolutional neural networks show promise for automating breast cancer detection from medical images to address increasing caseloads. Previous research has developed CNN approaches for segmenting breast tissue and detecting cancer from mammograms and ultrasound images with strong accuracy. This literature review evaluates past works applying deep learning to breast cancer detection and segmentation tasks to inform the development of a new CNN-based approach detailed in future work.
IRJET- Intelligent Prediction of Lung Cancer Via MRI Images using Morphologic...IRJET Journal
The document describes a proposed system to intelligently predict lung cancer using MRI images and morphological neural network analysis. The proposed system uses a three-stage approach: preprocessing MRI images, extracting features using wavelet decomposition and normalization, and classifying tissues as normal or abnormal using a morphological neural network with image pruning. This combination of morphological image processing and neural networks is intended to more efficiently classify cancer cells and identify affected regions than previous methods.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
Shap Analysis Based Gastric Cancer DetectionIRJET Journal
This document proposes a novel deep learning framework to detect gastric cancer from endoscopic images of the stomach. The framework uses a patch-based analysis where features are extracted from image patches and evaluated for cancer risk. A bag-of-features technique is then applied to the extracted features from selected patches for analysis. Experimental results show the proposed framework can effectively and efficiently detect gastric cancer from images and identify minute lesions. It achieves higher accuracy than other models using the same dataset. The framework is also more accurate than existing methods for gastric cancer detection.
This document summarizes imaging techniques for colorectal cancer, including double-contrast barium enema, colonoscopy, and CT colonography (virtual colonoscopy). It discusses the benefits and limitations of each technique. For CT colonography, it reviews literature on its accuracy in detecting polyps and cancers in screening populations. Key factors that influence CT colonography results like bowel preparation, insufflation method, and image viewing technique are also summarized. The document concludes that further standardization of the CT colonography technique and additional large clinical trials are needed to establish it as a screening tool.
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
PNEUMONIA DIAGNOSIS USING CHEST X-RAY IMAGES AND CNNIRJET Journal
This document discusses using convolutional neural networks to diagnose pneumonia from chest x-ray images. Specifically, it summarizes several research papers that used CNN models like InceptionV3 to extract features from x-ray images and then trained classification algorithms like support vector machines, neural networks, and K-nearest neighbors to classify images as pneumonia or normal. The neural network model achieved 84.1% sensitivity while support vector machines obtained the highest AUC of 93.1%. In general, CNNs can accurately diagnose pneumonia from x-rays but training the models requires a large dataset and computing resources.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Breast Cancer Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict breast cancer from patient data and imaging results. It first provides background on breast cancer, noting it is the most commonly diagnosed cancer worldwide. The document then reviews prior works applying machine learning to breast cancer prediction, finding support vector machines achieved the highest accuracy. It describes the dataset used, from the University of Wisconsin, containing patient data and tumor characteristics. Finally, it explores the data and discusses implementing classification algorithms like logistic regression, support vector machines, random forests and neural networks to predict cancer type, finding logistic regression achieved the highest accuracy of 98.24%.
Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidd...Christo Ananth
Christo Ananth, S. Amutha, K. Niha, Djabbarov Botirjon Begimovich, “Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidden Markov Random Fields with Expectation Maximization (HMRF-EM)”, International Journal of Early Childhood Special Education, Volume 14, Issue 05, 2022,pp. 2400-2410.
Christo Ananth et al. discussed that In surgical planning and cancer treatment, it is crucial to segment and measure a liver tumor's volume accurately. Because it would involve automation, standardisation, and the incorporation of complete volumetric information, accurate automatic liver tumor segmentation would substantially affect the processes for therapy planning and follow-up reporting. Based on the Hidden Markov random field, Automatic liver tumor detection in CT scans is possible using hidden Markov random fields (HMRF-EM). A CT scan of the liver may be too low-resolution for this software. CT liver tissue segmentation is based on the HMRF model. When building an accurate HMRF model, an accurate initial image estimate is crucial. Adaptive K-means clustering is often used for initial estimation. HMRF's performance can be greatly improved by clustering. This project aims to segment liver tissue quickly. This paper proposes an adaptive K-means clustering approach for estimating liver images in the HMRF-EM model. The previous strategy had flaws, so this one fixed them. We compare the current and proposed methods. The proposed method outperforms the currently used method.
Oral cancer is the most significant and growing concern worldwide. It ranks as 3rd in India and 8th
largest prevalent form of cancer in world. Oral cancer is often diagnosed, only after reached to an untreatable
stage. Early detection and prevention are the major objectives to control the oral cancer. Histopathology
analysis of biopsied lesion followed by visual examination is the current clinical procedure. This procedure is
invasive and requires a waiting period for the diagnostic results. Thus, there is a need to develop a non-invasive
screening device for oral cancer detection. Optical imaging has emerged as effective tool for detecting
malignant changes associated with oral cancer and also effective in assisting with the detection of oral mucosal
abnormalities. Hence, this paper focuses on development of non-invasive, real-time diagnostic tool based on
optical imaging technique in which involves - fluorescence emission and diffuse reflectance imaging modalities
for screening of oral cancer.
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer...IRJET Journal
The document discusses using supervised machine learning algorithms to evaluate the performance of breast cancer diagnosis. It evaluates algorithms like perceptron, cascade-forward backpropagation, and feed-forward backpropagation on a breast cancer dataset from the Wisconsin Breast Cancer Diagnosis database. The algorithms are used to develop a process for diagnosis and prediction of breast cancer that could help physicians diagnose the disease more accurately.
IRJET- Survey Paper on Oral Cancer Detection using Machine LearningIRJET Journal
This document discusses several papers on using machine learning techniques for oral cancer detection. It first provides background on oral cancer and the importance of early detection. It then summarizes five research papers that used different machine learning and data mining approaches for oral cancer classification and detection, including using algorithms like Naive Bayes, J48, and SVM on clinical datasets, as well as analyzing oral microbiome data using metagenomics and machine learning models. The goal is to evaluate machine learning as a domain for early oral cancer detection by analyzing patient datasets and developing predictive and classification rules.
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
Decomposition of color wavelet with higher order statistical texture and conv...IJECEIAES
Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy, but the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ=0 0 , 45 0 , 90 0 , 135 0 ). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases.
IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Netwo...IRJET Journal
1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
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.
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
This document summarizes a research paper that proposes a deep learning method for breast cancer histological image segmentation and classification. The method uses a feature pyramid network to extract features from histological images, which are then optimized using a grasshopper optimization algorithm. The optimized features are used with a segmenting objects by locations convolutional network for nucleus segmentation. For classification, the features are passed through fully connected layers. The method achieves 88.46% segmentation accuracy and 99.2% classification accuracy on a large breast cancer image dataset. It provides an effective computer-aided approach for breast cancer analysis in a medical setting.
DIFFERENT IMAGING MODALITIES USED FOR THE DETECTION OF PROSTATE CANCER – A RE...IRJET Journal
The document discusses various imaging modalities used to detect prostate cancer, including multiparametric ultrasound, multiparametric MRI, MRI-ultrasound fusion imaging, and positron emission tomography. It provides details on prostate anatomy, cancer grading, and treatment options to provide context. The modalities are compared in terms of their ability to detect characteristics like tissue alterations, angiogenesis, and metastatic spread. Limitations and potential improvements to the modalities are also reviewed.
PREDICTION OF BREAST CANCER,COMPARATIVE REVIEW OF MACHINE LEARNING TECHNIQUES...IRJET Journal
This document presents a comparative review of machine learning techniques for breast cancer prediction. It analyzes several algorithms applied to the Wisconsin Breast Cancer Diagnosis (WBCD) dataset, including SVM, KNN, Random Forest, AdaBoost, and XGBoost. XGBoost achieved the highest accuracy of 98.24% with 0% false negatives. The study demonstrates that machine learning can effectively predict breast cancer and increase early detection accuracy.
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUEIRJET Journal
1) The document discusses a method for detecting diabetic retinal disease using integrated shallow convolutional neural networks, which can improve classification accuracy by 3% on small datasets compared to other CNN techniques.
2) It aims to classify retinal images to detect diabetic retinopathy through shallow CNNs, focusing on cases with limited labelled training data, as deep CNNs typically require large datasets for high accuracy.
3) Experimental results show the proposed approach reduces time cost to around 30% of the smallest dataset tested, which is 10% of the original dataset, while maintaining classification accuracy compared to other integrated CNN learning algorithms.
A convenient clinical nomogram for small intestine adenocarcinomanguyên anh doanh
The document describes a study that developed a nomogram to predict cancer-specific survival for patients with small-intestine adenocarcinoma. Researchers analyzed data on 4,971 patients from the SEER database and identified 8 factors associated with survival: age, sex, marital status, insurance status, grade, stage, surgery status, and chemotherapy. These factors were used to create a nomogram that assigns a score to each variable to predict 3- and 5-year survival probabilities. Validation tests found the nomogram predicted survival more accurately than the AJCC staging system and closely matched actual survival rates.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
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Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
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This document discusses using machine learning algorithms to predict breast cancer from patient data and imaging results. It first provides background on breast cancer, noting it is the most commonly diagnosed cancer worldwide. The document then reviews prior works applying machine learning to breast cancer prediction, finding support vector machines achieved the highest accuracy. It describes the dataset used, from the University of Wisconsin, containing patient data and tumor characteristics. Finally, it explores the data and discusses implementing classification algorithms like logistic regression, support vector machines, random forests and neural networks to predict cancer type, finding logistic regression achieved the highest accuracy of 98.24%.
Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidd...Christo Ananth
Christo Ananth, S. Amutha, K. Niha, Djabbarov Botirjon Begimovich, “Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidden Markov Random Fields with Expectation Maximization (HMRF-EM)”, International Journal of Early Childhood Special Education, Volume 14, Issue 05, 2022,pp. 2400-2410.
Christo Ananth et al. discussed that In surgical planning and cancer treatment, it is crucial to segment and measure a liver tumor's volume accurately. Because it would involve automation, standardisation, and the incorporation of complete volumetric information, accurate automatic liver tumor segmentation would substantially affect the processes for therapy planning and follow-up reporting. Based on the Hidden Markov random field, Automatic liver tumor detection in CT scans is possible using hidden Markov random fields (HMRF-EM). A CT scan of the liver may be too low-resolution for this software. CT liver tissue segmentation is based on the HMRF model. When building an accurate HMRF model, an accurate initial image estimate is crucial. Adaptive K-means clustering is often used for initial estimation. HMRF's performance can be greatly improved by clustering. This project aims to segment liver tissue quickly. This paper proposes an adaptive K-means clustering approach for estimating liver images in the HMRF-EM model. The previous strategy had flaws, so this one fixed them. We compare the current and proposed methods. The proposed method outperforms the currently used method.
Oral cancer is the most significant and growing concern worldwide. It ranks as 3rd in India and 8th
largest prevalent form of cancer in world. Oral cancer is often diagnosed, only after reached to an untreatable
stage. Early detection and prevention are the major objectives to control the oral cancer. Histopathology
analysis of biopsied lesion followed by visual examination is the current clinical procedure. This procedure is
invasive and requires a waiting period for the diagnostic results. Thus, there is a need to develop a non-invasive
screening device for oral cancer detection. Optical imaging has emerged as effective tool for detecting
malignant changes associated with oral cancer and also effective in assisting with the detection of oral mucosal
abnormalities. Hence, this paper focuses on development of non-invasive, real-time diagnostic tool based on
optical imaging technique in which involves - fluorescence emission and diffuse reflectance imaging modalities
for screening of oral cancer.
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This document discusses several papers on using machine learning techniques for oral cancer detection. It first provides background on oral cancer and the importance of early detection. It then summarizes five research papers that used different machine learning and data mining approaches for oral cancer classification and detection, including using algorithms like Naive Bayes, J48, and SVM on clinical datasets, as well as analyzing oral microbiome data using metagenomics and machine learning models. The goal is to evaluate machine learning as a domain for early oral cancer detection by analyzing patient datasets and developing predictive and classification rules.
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Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy, but the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ=0 0 , 45 0 , 90 0 , 135 0 ). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases.
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1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
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The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
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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.
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
This document summarizes a research paper that proposes a deep learning method for breast cancer histological image segmentation and classification. The method uses a feature pyramid network to extract features from histological images, which are then optimized using a grasshopper optimization algorithm. The optimized features are used with a segmenting objects by locations convolutional network for nucleus segmentation. For classification, the features are passed through fully connected layers. The method achieves 88.46% segmentation accuracy and 99.2% classification accuracy on a large breast cancer image dataset. It provides an effective computer-aided approach for breast cancer analysis in a medical setting.
DIFFERENT IMAGING MODALITIES USED FOR THE DETECTION OF PROSTATE CANCER – A RE...IRJET Journal
The document discusses various imaging modalities used to detect prostate cancer, including multiparametric ultrasound, multiparametric MRI, MRI-ultrasound fusion imaging, and positron emission tomography. It provides details on prostate anatomy, cancer grading, and treatment options to provide context. The modalities are compared in terms of their ability to detect characteristics like tissue alterations, angiogenesis, and metastatic spread. Limitations and potential improvements to the modalities are also reviewed.
PREDICTION OF BREAST CANCER,COMPARATIVE REVIEW OF MACHINE LEARNING TECHNIQUES...IRJET Journal
This document presents a comparative review of machine learning techniques for breast cancer prediction. It analyzes several algorithms applied to the Wisconsin Breast Cancer Diagnosis (WBCD) dataset, including SVM, KNN, Random Forest, AdaBoost, and XGBoost. XGBoost achieved the highest accuracy of 98.24% with 0% false negatives. The study demonstrates that machine learning can effectively predict breast cancer and increase early detection accuracy.
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUEIRJET Journal
1) The document discusses a method for detecting diabetic retinal disease using integrated shallow convolutional neural networks, which can improve classification accuracy by 3% on small datasets compared to other CNN techniques.
2) It aims to classify retinal images to detect diabetic retinopathy through shallow CNNs, focusing on cases with limited labelled training data, as deep CNNs typically require large datasets for high accuracy.
3) Experimental results show the proposed approach reduces time cost to around 30% of the smallest dataset tested, which is 10% of the original dataset, while maintaining classification accuracy compared to other integrated CNN learning algorithms.
A convenient clinical nomogram for small intestine adenocarcinomanguyên anh doanh
The document describes a study that developed a nomogram to predict cancer-specific survival for patients with small-intestine adenocarcinoma. Researchers analyzed data on 4,971 patients from the SEER database and identified 8 factors associated with survival: age, sex, marital status, insurance status, grade, stage, surgery status, and chemotherapy. These factors were used to create a nomogram that assigns a score to each variable to predict 3- and 5-year survival probabilities. Validation tests found the nomogram predicted survival more accurately than the AJCC staging system and closely matched actual survival rates.
Similar to Oral Cancer Detection Using Image Processing and Deep Neural Networks. (18)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.