The document describes a student project that aims to classify and detect lung disorders from chest x-rays using generative adversarial networks (GANs). It provides an overview of the problem statement, literature review on existing methodologies, and the proposed system architecture. The problem is that early diagnosis of lung diseases is important but current methods are time-consuming. The project aims to develop a hybrid GAN model to overcome issues in existing models and accurately detect lung disorders from x-rays. It will classify diseases like pneumonia and lung cancer.
Built a CNN based machine learning model to diagnose Pneumonia disease using chest x-rays.
Methodologies & Tools: KDD, Python, VGG19 model, Convolutional Neural Network.
This document describes a proposed method for classifying chest x-ray images to diagnose lung infections using convolutional neural networks (CNNs). The objectives are to examine if transfer learning from different source domains can improve performance for classifying healthy, pneumonia and COVID-19 cases using a small dataset. The proposed methodology includes collecting datasets, training a CNN model using transfer learning, evaluating performance using a confusion matrix, and identifying opportunities for future enhancement like exploring different network architectures and domains.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
This document summarizes Mansi Chowkkar's MSc research project on detecting breast cancer from histopathological images using deep learning and transfer learning. The research aims to classify images as malignant or benign with high accuracy and efficiency. It implements CNN and DenseNet-121 models, with the latter using transfer learning with pre-trained ImageNet weights. The research achieved 90.9% test accuracy with CNN and 88.03% accuracy with transfer learning. Related work discusses deep learning applications in healthcare, image processing techniques, prior research on DenseNet, and the use of transfer learning for medical image classification with limited data.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
Pneumonia Detection Using Deep Learning and Transfer LearningIRJET Journal
This document presents research on using deep learning and transfer learning techniques to detect pneumonia from chest x-ray images. The researchers trained several models, including CNNs, DenseNet, VGG-16, ResNet, and InceptionNet on a dataset of chest x-rays labeled as normal or pneumonia. The models achieved accuracy in detecting pneumonia of 89.6-97%, depending on the specific model. The researchers found that deep learning approaches like these have significant potential to improve the accuracy and efficiency of pneumonia diagnosis compared to traditional methods. Overall, the study demonstrated promising results for using machine and deep learning to classify medical images and detect health conditions like pneumonia.
Built a CNN based machine learning model to diagnose Pneumonia disease using chest x-rays.
Methodologies & Tools: KDD, Python, VGG19 model, Convolutional Neural Network.
This document describes a proposed method for classifying chest x-ray images to diagnose lung infections using convolutional neural networks (CNNs). The objectives are to examine if transfer learning from different source domains can improve performance for classifying healthy, pneumonia and COVID-19 cases using a small dataset. The proposed methodology includes collecting datasets, training a CNN model using transfer learning, evaluating performance using a confusion matrix, and identifying opportunities for future enhancement like exploring different network architectures and domains.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
This document summarizes Mansi Chowkkar's MSc research project on detecting breast cancer from histopathological images using deep learning and transfer learning. The research aims to classify images as malignant or benign with high accuracy and efficiency. It implements CNN and DenseNet-121 models, with the latter using transfer learning with pre-trained ImageNet weights. The research achieved 90.9% test accuracy with CNN and 88.03% accuracy with transfer learning. Related work discusses deep learning applications in healthcare, image processing techniques, prior research on DenseNet, and the use of transfer learning for medical image classification with limited data.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
Pneumonia Detection Using Deep Learning and Transfer LearningIRJET Journal
This document presents research on using deep learning and transfer learning techniques to detect pneumonia from chest x-ray images. The researchers trained several models, including CNNs, DenseNet, VGG-16, ResNet, and InceptionNet on a dataset of chest x-rays labeled as normal or pneumonia. The models achieved accuracy in detecting pneumonia of 89.6-97%, depending on the specific model. The researchers found that deep learning approaches like these have significant potential to improve the accuracy and efficiency of pneumonia diagnosis compared to traditional methods. Overall, the study demonstrated promising results for using machine and deep learning to classify medical images and detect health conditions like pneumonia.
Breast cancer classification with histopathological image based on machine le...IJECEIAES
This document summarizes a study that used pre-trained convolutional neural network (CNN) models to classify breast cancer histopathology images as benign or malignant. Five CNN architectures - ResNet-50, VGG-19, Inception-V3, AlexNet, and ResNet-50 as a feature extractor combined with random forest and k-nearest neighbors classifiers - were evaluated on the publicly available BreakHis dataset. The ResNet-50 network achieved the highest test accuracy of 97% for classifying the breast cancer images.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
Cervical Cancer Detection: An Enhanced Approach through Transfer Learning and...IRJET Journal
This document presents research on using the DenseNet169 deep learning model for cervical cancer detection. The researcher trained and tested the model on a large cervical cell image dataset from Kaggle. Through data preprocessing like augmentation and normalization, and transfer learning by fine-tuning a DenseNet pre-trained on ImageNet, the model achieved 95.27% accuracy in classifying five cervical cell types. Evaluation of the model showed high average precision, recall, and F1-score, demonstrating its ability to correctly classify different cervical cell images. The research highlights the potential of deep learning models for automating cervical cancer screening and improving early detection.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Using Deep Learning and Transfer Learning for Pneumonia DetectionIRJET Journal
This document presents research on using deep learning and transfer learning models to detect pneumonia from chest x-ray images. The researchers collected a dataset of over 5,000 chest x-rays and split it into training and test sets. Data augmentation techniques were used to expand the training dataset size. Several deep learning models were trained on the data including CNN, DenseNet121, VGG16, ResNet50, and Inception v3. Transfer learning was also utilized by training models pre-trained on ImageNet. The Inception v3 model achieved the highest testing accuracy of 80.29% for pneumonia detection. The researchers concluded the deep learning models could help radiologists diagnose pneumonia but that more work is needed to localize affected lung regions.
Generalized deep learning model for Classification of Gastric, Colon and Rena...IRJET Journal
This document proposes developing a generalized deep learning model to classify gastric, colon, and renal cancer using a single model. The model would be trained on whole slide images of tissue samples fed through an EfficientNet model pre-trained on ImageNet. The model would be trained using transfer learning with partial transfusion to demonstrate the ability to classify pathology images from different sites. Previous studies have developed models to classify individual tissue types but not a unified model. The proposed model aims to address situations where the tissue site of origin is unknown.
Pneumonia Detection Using Convolutional Neural Network WritersIRJET Journal
This document describes a study that developed a convolutional neural network (CNN) model to detect and classify pneumonia from chest x-ray images without any training. The model was able to extract relevant features from chest x-ray images and determine if a patient has pneumonia. The CNN model achieved high accuracy for pneumonia classification compared to other advanced methods that rely on transfer learning or manual feature engineering. The study used a dataset of 5,500 chest x-ray images and performed image processing techniques like background removal and cropping before extracting features with the CNN and classifying images.
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.
A Comparative Study of Various Machine Learning Techniques for Brain Tumor De...IRJET Journal
This document summarizes several studies that evaluated various machine learning techniques for detecting brain tumors using medical imaging data. It finds that convolutional neural networks (CNNs) consistently achieved the highest accuracy rates, ranging from 79-97.7%. The document reviews studies applying techniques like K-means clustering, support vector machines, random forests, and CNNs to datasets from sources like the UCI repository and hospitals. Most accurate were CNN models, with some achieving over 90% accuracy at detecting brain tumors in MRI images. The document concludes CNNs have demonstrated great effectiveness in medical applications like bioinformatics and brain tumor detection.
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.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
The document discusses machine learning methods for lung cancer detection using CT scans. It first provides background on lung cancer and the need for early detection. It then describes datasets used, including the LIDC-IDRI and Kaggle datasets containing labeled lung CT scans. Machine learning algorithms explored for segmentation include U-Net convolutional networks and for classification include logistic regression, naive Bayes, SVM, random forest and gradient boosting. Performance is evaluated based on sensitivity, specificity and other metrics. Overall results show machine learning methods achieving detection and classification performance comparable to radiologists while reducing false positives.
ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors.
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.
YOLOv8-Based Lung Nodule Detection: A Novel Hybrid Deep Learning Model ProposalIRJET Journal
This document proposes a novel deep learning model for automated real-time detection of lung nodules using chest CT scans. A two-stage model is proposed that first uses a CNN to detect nodule regions with 94% accuracy, then fine-tunes a YOLOv8 object detection model on the detected regions. When tested on the LUNA16 dataset, the YOLOv8m configuration achieved 92.3% accuracy, 88.5% sensitivity, and 53.5% mean average precision for nodule detection, outperforming existing methods. The proposed hybrid model shows potential for improving nodule detection accuracy and efficiency for early lung cancer screening.
The document describes using a convolutional neural network with the VGG16 architecture to classify lung cancer CT scan images into 4 classes: large cell carcinoma, squamous cell carcinoma, adenocarcinoma, and normal lungs. The model is trained on a dataset of 1000 CT scan images from Kaggle and achieves an AUC of 0.94, indicating high accuracy in identifying different types of lung cancer. This CNN model with pre-trained VGG16 weights provides an effective approach for classifying lung cancer images and could help enable early diagnosis and treatment of lung cancer.
A REVIEW PAPER ON PULMONARY NODULE DETECTIONIRJET Journal
This document reviews different techniques for pulmonary nodule detection in CT scans using deep learning. It summarizes several papers that have used techniques like convolutional neural networks (CNNs), 3D CNNs, and customized mixed link networks to develop computer-aided diagnosis systems for detecting and classifying lung nodules. These papers report accuracy rates from 85.7% to 98.7% and sensitivities from 80.06% to 94% depending on the specific deep learning approach and dataset used. The document concludes by comparing the performance of these different papers.
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
Sigma Xi Student Showcase - Fast Pre-Diagnosis of Breast Cancer Using CNNsMichael Batavia
This document proposes using a convolutional neural network (CNN) to classify breast cancer tumors in lymph nodes and maximize neural network accuracy. The author developed a custom CNN with parallel processing across multiple GPUs that achieved 83% validation accuracy on a breast cancer dataset, outperforming pathologist classification by 10%. A separate experiment found that images with 40x magnification led to statistically better accuracy than 20x magnification images. The CNN architecture, datasets used, training methodology, and results are described to support using CNNs for early and individualized breast cancer diagnosis.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Breast cancer classification with histopathological image based on machine le...IJECEIAES
This document summarizes a study that used pre-trained convolutional neural network (CNN) models to classify breast cancer histopathology images as benign or malignant. Five CNN architectures - ResNet-50, VGG-19, Inception-V3, AlexNet, and ResNet-50 as a feature extractor combined with random forest and k-nearest neighbors classifiers - were evaluated on the publicly available BreakHis dataset. The ResNet-50 network achieved the highest test accuracy of 97% for classifying the breast cancer images.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
Cervical Cancer Detection: An Enhanced Approach through Transfer Learning and...IRJET Journal
This document presents research on using the DenseNet169 deep learning model for cervical cancer detection. The researcher trained and tested the model on a large cervical cell image dataset from Kaggle. Through data preprocessing like augmentation and normalization, and transfer learning by fine-tuning a DenseNet pre-trained on ImageNet, the model achieved 95.27% accuracy in classifying five cervical cell types. Evaluation of the model showed high average precision, recall, and F1-score, demonstrating its ability to correctly classify different cervical cell images. The research highlights the potential of deep learning models for automating cervical cancer screening and improving early detection.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Using Deep Learning and Transfer Learning for Pneumonia DetectionIRJET Journal
This document presents research on using deep learning and transfer learning models to detect pneumonia from chest x-ray images. The researchers collected a dataset of over 5,000 chest x-rays and split it into training and test sets. Data augmentation techniques were used to expand the training dataset size. Several deep learning models were trained on the data including CNN, DenseNet121, VGG16, ResNet50, and Inception v3. Transfer learning was also utilized by training models pre-trained on ImageNet. The Inception v3 model achieved the highest testing accuracy of 80.29% for pneumonia detection. The researchers concluded the deep learning models could help radiologists diagnose pneumonia but that more work is needed to localize affected lung regions.
Generalized deep learning model for Classification of Gastric, Colon and Rena...IRJET Journal
This document proposes developing a generalized deep learning model to classify gastric, colon, and renal cancer using a single model. The model would be trained on whole slide images of tissue samples fed through an EfficientNet model pre-trained on ImageNet. The model would be trained using transfer learning with partial transfusion to demonstrate the ability to classify pathology images from different sites. Previous studies have developed models to classify individual tissue types but not a unified model. The proposed model aims to address situations where the tissue site of origin is unknown.
Pneumonia Detection Using Convolutional Neural Network WritersIRJET Journal
This document describes a study that developed a convolutional neural network (CNN) model to detect and classify pneumonia from chest x-ray images without any training. The model was able to extract relevant features from chest x-ray images and determine if a patient has pneumonia. The CNN model achieved high accuracy for pneumonia classification compared to other advanced methods that rely on transfer learning or manual feature engineering. The study used a dataset of 5,500 chest x-ray images and performed image processing techniques like background removal and cropping before extracting features with the CNN and classifying images.
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.
A Comparative Study of Various Machine Learning Techniques for Brain Tumor De...IRJET Journal
This document summarizes several studies that evaluated various machine learning techniques for detecting brain tumors using medical imaging data. It finds that convolutional neural networks (CNNs) consistently achieved the highest accuracy rates, ranging from 79-97.7%. The document reviews studies applying techniques like K-means clustering, support vector machines, random forests, and CNNs to datasets from sources like the UCI repository and hospitals. Most accurate were CNN models, with some achieving over 90% accuracy at detecting brain tumors in MRI images. The document concludes CNNs have demonstrated great effectiveness in medical applications like bioinformatics and brain tumor detection.
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.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
The document discusses machine learning methods for lung cancer detection using CT scans. It first provides background on lung cancer and the need for early detection. It then describes datasets used, including the LIDC-IDRI and Kaggle datasets containing labeled lung CT scans. Machine learning algorithms explored for segmentation include U-Net convolutional networks and for classification include logistic regression, naive Bayes, SVM, random forest and gradient boosting. Performance is evaluated based on sensitivity, specificity and other metrics. Overall results show machine learning methods achieving detection and classification performance comparable to radiologists while reducing false positives.
ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors.
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.
YOLOv8-Based Lung Nodule Detection: A Novel Hybrid Deep Learning Model ProposalIRJET Journal
This document proposes a novel deep learning model for automated real-time detection of lung nodules using chest CT scans. A two-stage model is proposed that first uses a CNN to detect nodule regions with 94% accuracy, then fine-tunes a YOLOv8 object detection model on the detected regions. When tested on the LUNA16 dataset, the YOLOv8m configuration achieved 92.3% accuracy, 88.5% sensitivity, and 53.5% mean average precision for nodule detection, outperforming existing methods. The proposed hybrid model shows potential for improving nodule detection accuracy and efficiency for early lung cancer screening.
The document describes using a convolutional neural network with the VGG16 architecture to classify lung cancer CT scan images into 4 classes: large cell carcinoma, squamous cell carcinoma, adenocarcinoma, and normal lungs. The model is trained on a dataset of 1000 CT scan images from Kaggle and achieves an AUC of 0.94, indicating high accuracy in identifying different types of lung cancer. This CNN model with pre-trained VGG16 weights provides an effective approach for classifying lung cancer images and could help enable early diagnosis and treatment of lung cancer.
A REVIEW PAPER ON PULMONARY NODULE DETECTIONIRJET Journal
This document reviews different techniques for pulmonary nodule detection in CT scans using deep learning. It summarizes several papers that have used techniques like convolutional neural networks (CNNs), 3D CNNs, and customized mixed link networks to develop computer-aided diagnosis systems for detecting and classifying lung nodules. These papers report accuracy rates from 85.7% to 98.7% and sensitivities from 80.06% to 94% depending on the specific deep learning approach and dataset used. The document concludes by comparing the performance of these different papers.
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
Sigma Xi Student Showcase - Fast Pre-Diagnosis of Breast Cancer Using CNNsMichael Batavia
This document proposes using a convolutional neural network (CNN) to classify breast cancer tumors in lymph nodes and maximize neural network accuracy. The author developed a custom CNN with parallel processing across multiple GPUs that achieved 83% validation accuracy on a breast cancer dataset, outperforming pathologist classification by 10%. A separate experiment found that images with 40x magnification led to statistically better accuracy than 20x magnification images. The CNN architecture, datasets used, training methodology, and results are described to support using CNNs for early and individualized breast cancer diagnosis.
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1. Lung-Disorder Classification and Detection from Chest-Xray using
GAN’S
Under the guidance of
Prof. Richa Sharma
Project Guide
Department of Information Technology
DJSCE
Mumbai University
2022-23
Abhinav Patel
60003190002
Janmey Patel
60003190029
Hardik Patel
60003190019
2. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 2
Name of the project/Thesis
Overview
❑Problem Statement
● Problem
● Motivation & Scope
● Aim and problem definition
❑Literature Review
● Existing Methodologies
● Models and Methodologies
❑System Architecture
❑Implementation & Paper publish Status
❑References
3. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 3
Name of the project/Thesis
Problem
❑ Major diseases that affect the lungs are
pneumonia, tuberculosis, COPD (chronic
obstructive pulmonary disease), Lung
Cancer and COVIP-19.
❑ Lung diseases are a severe matter of
concern all over the world.
❑ Early diagnosis with the advanced
methods and technologies has become
crucial to help in faster recovery and
improve long-term survival rates.
❑ Damage to the lungs cannot be
reversed. Delayed diagnosis results in
delayed treatment and smoking-cessation
intervention, so early and accurate
diagnosis is a window of opportunity to
make a real difference to a patient's life.
4. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 4
Name of the project/Thesis
Motivation
❑ The manual analysis of x-ray images is a long
process requiring radiological expertise and a
large volume of time. Deep learning can play a
crucial role in exceeding decision making,
detecting marks of disease as well as conducting
the initial examination and suggesting urgent
cases.
❑ DL techniques are known to require very large
amounts of data to train the neural networks
(NN), which can sometimes be a problem due to
limited data availability.
5. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 5
Name of the project/Thesis
Scope
❑ Generative Adversarial Network (GAN) is a type of generative model based on deep
neural networks. This technique is known for learning to generate new data with the same
statistics as the training set.
❑ By using GANs to generate synthetic medical data, we will be able to create new datasets
and share them in the medical community.
❑ We believe that hospitals could find this solution useful, especially due to data
confidentiality.
WHY GANS ?
❑ Limited Data Availability
❑ Unlabelled datasets and class imbalance
❑ More sharper and discrete outputs.
6. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 6
Name of the project/Thesis
Aim
AIM: To develop a hybrid Gan based model to overcome the issues in existing models
and detect lung disorders in the input X-Ray images.
Diseases to be detected:
1. Pneumonia
2. Lung Cancer
Problem Definition: To study the performance of a new hybrid model that can accurately
detect and classify the lung disorders. We believe that the new model will prove to be better
than the existing ones and can be conveniently used in the clinics.
7. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 7
Name of the project/Thesis
Literature Review
❑ The first CAD system for detecting lung nodules or affected lung cells in the late 1980s but those efforts
were not enough. WHY? Inadequate computational resources for the implementation of advanced
image processing techniques. It is also much time consuming.
❑ A 3D deep CNN is proposed with multiscale prediction strategies in order to detect the lung nodules
from segmented images .However, the work cannot classify disease types and the multiscale prediction
approaches.
❑ A system is built on deep learning based computer aided diagnosis. Deep learning based CAD system
is used for the clinically significant detection of pulmonary masses/nodules on chest X-ray images.
❑ Moreover, deep learning method is also proposed in another paper where several transfer learning
methods are used for pneumonia diagnoses, but the parameter tuning for their implemented methods
are very complex.
8. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 8
Name of the project/Thesis
Literature Review and Existing System
❑ Lung-GANs: Unsupervised Representation Learning for Lung Disease Classification
Using Chest CT and X-Ray Images
❑ CNN architecture for both models. The proposed architecture is trained and tested using a DL and reinforcement
learning (RL) library called TensorLayer. The base learners are trained using Random Forest and Linear Support
Vector Classification.
❑ Linear SVC and random forest were used as base classifiers. These algorithms use different methods to represent
the knowledge, and thus the hypothesis space is explored from different perspectives. As a result, when their
predictions are combined, the resultant classifier achieves better accuracy than each individual classifier.
❑ The classification performance of the proposed Lung-GANs and three other existing unsupervised methods was
compared and Lung-GANs achieved the highest classification accuracy on all datasets used in this work.
❑ Proved 10.7% more accurate than DCEC (unsupervised deep clustering algorithm that incorporates convolutional
neural networks).
❑ The method achieved 97.6% average accuracy compared to the 93.8% average accuracy obtained by the
autoencoder.
❑ It reported higher accuracy (up to 99.5%) and better sensitivity compared to the existing methods on six different
lung disease datasets. In conclusion, Lung-GANs provide a noteworthy improvement in computer-aided diagnosis
of lung diseases.
9. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 9
Name of the project/Thesis
Literature Review and Existing System
❑ A generalized framework for lung Cancer classification based on deep generative
models (Non-Gan Approach)
❑ Two deep learning models - Generative and ResNet50.
❑ RestNet50 is pre-trained on the ImageNet dataset. Certain parameters like the number of iterations and the
learning rate were set in order to begin fine-tuning the CXR lung dataset.
❑ The deep convolutional neural network is used as a classifier model. It can be concluded that deep learning
models can accurately classify XCR lung pictures. Increasing the number of training samples increases the system
accuracy due to the increase of the quality of the learned model. The proposed system’s performance saturates
after about 4000 samples.
❑ The classifier takes 1.2334 s on average to classify a single image using a machine with 13GB RAM.
❑ Proposed framework acquires 98.91% overall detection accuracy, 98.46% accuracy, 97.72% precision.
10. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 10
Name of the project/Thesis
Literature Review and Existing System
❑ Combination of GAN and CNN for automatic subcentimeter pulmonary
adenocarcinoma classification - Wang - Quantitative Imaging in Medicine and
Surgery
❑ First they compared and integrated the different GAN techniques. Then designed a CNN for classification.
The results suggested that GAN has the potential to alleviate the data insufficiency problem and to improve
the classification performance of the CNN. CNN model is also convenient for implementation in hospital
diagnostic systems and can promote the development of precision medical care.
❑ They followed the below 4 steps:
Data Collection - collecting datasets, confirming the classifications through surveys.
Data processing - Deciding on the raw data, data augmentation techniques and rescaling.
Gan based image synthesis - Testing the three GAN techniques- wGAN-gp, pix2pix and pgGAN.
CNN classification - Testing and comparing various network structures and deciding on the best.
❑ Observations:
First, the performance of the CNN was unstable and inadequate when only using the raw dataset.
Second, an additional dataset significantly improved the performance of the CNN.
Lastly, the CNN performed best with the dataset generated by the progressive-growing wGAN.
11. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 11
Name of the project/Thesis
Literature Review and Existing System
❑ Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2
Model and Edge Computing
❑ Dataset is trained by hybrid class balancing with the help of Oversampling and Proportionate class weights
as the current dataset is in the ratio of 1:3:8 (Viral Pneumonia:COVID-19:Normal).
❑ The class imbalance in the dataset was tackled with a combination of the synthetic minority over-sampling
technique (SMOTE) and weighted class balancing. CNN hyper parameter tuning is used to reduce the
training time.
❑ TL models such as SqueezeNet, VGG19, ResNet50, and MobileNetV2 have accuracy of 97.33 percent, 91.66
percent, 90.33 percent, and 76.00 percent. DL model that was trained from scratch has an accuracy of 92.43
percent.
❑ Support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented
gradients (DT + HOG) are two feature-based ML classification techniques that have accuracy of 87.98 percent
and 86.87 percent.
❑ The best accuracy comes from a hybrid Inception-ResNet-v2 transfer learning model. Data augmentation and
image enhancement help in improving the accuracy of disease classification tasks. The proposed technique has
a 98.66 percent average accuracy.
12. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 12
Name of the project/Thesis
Related research
TYPES OF GANS :
There are multiple types of GANs that perform different applications but these are some of the important GANs.
1. Vanilla GAN
2. Conditional Gan (CGAN):
3. Deep Convolutional GAN (DCGAN) : preferable as deep learning networks are implemented.
4. CycleGAN:
5. Style GAN:
6. Super Resolution GAN (SRGAN)
13. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 13
Name of the project/Thesis
Models and Methodologies
Models and their Accuracies:
Below are some of the models that have been used and implemented by researched specific to
various purposes.
Models Accuracy
VC Net 99.49% (Nodule detection)
ReSNET50 98.91% (Overall Model detection)
Modified Capsnet 63.8% (Non-GAN Data Validation)
RF and SVC 97.6% (Model Testing)
Vanilla RGB 69% (Non-GAN Data Validation)
CNN and VGG 94% (Overall Model detection)
14. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 14
Name of the project/Thesis
System Architecture
15. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 15
Name of the project/Thesis
Implementation Status
❑ Tried to understand the working of the GANs by training the
MNIST number dataset.
❑ This dataset consist of 60k entries which is divided into 50
epochs for training the dataset.
❑ A seed is used to produce an image. The discriminator is then
used to classify real images (drawn from the training set) and
fakes images (produced by the generator). The loss is
calculated for each of these models, and the gradients are used
to update the generator and discriminator.
❑ As the number of iterations to train the datasets increases, the
accuracy of the generator increases such that even the
discriminator cannot identify the difference between the
dataset and the synthetically generated images.
16. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 16
Name of the project/Thesis
Implementation Status
❑ We have implemented Deep
Convolutional GAN on the NIH
dataset.
❑ The dataset has 1,12,120 X-Ray
images which were passed through
the network.
❑ A set of candidate images is
considered for training the
network.
❑ Next, we are going to activate the
generative loss functions which
will help us to discriminate the
input image from the sample set.
17. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 17
Name of the project/Thesis
Implementation Status
18. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 18
Name of the project/Thesis
Paper Publish Status
19. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 19
Name of the project/Thesis
References
1. Chandra Mani Sharma, Lakshay Goyal, Vijayaraghavan M. Chariar, Navel Sharma, "Lung Disease Classification in CXR Images Using Hybrid
Inception-ResNet-v2 Model and Edge Computing", Journal of Healthcare Engineering, vol. 2022,
https://doi.org/10.1155/2022/9036457
2. Ren, Zeyu, Yudong Zhang, and Shuihua Wang. 2022. "A Hybrid Framework for Lung Cancer Classification" Electronics 11, no. 10: 1614.
https://pubmed.ncbi.nlm.nih.gov/32835077/
3. J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, r Ball, K. Shpanskaya, et al.
https://www.sciencedirect.com/science/article/pii/S2352914820300290#bbib17
4. A.A.A. Setio, A. Traverso, T. de Bel, M.S.N. Berens, C. van den Bogaard, P. Cerello, H. Chen, Q. Dou, M.E. Fantacci, B. Geurts, et al.
https://www.sciencedirect.com/science/article/pii/S2352914820300290#bbib12
5. Y. Gu, X. Lu, L. Yang, B. Zhang, D. Yu, Y. Zhao, L. Gao, L. Wu, T. Zhou
https://www.sciencedirect.com/science/article/pii/S2352914820300290#bib28
6. Chouhan, V.; Singh, S.K.; Khamparia, A.; Gupta, D.; Tiwari, P.; Moreira, C.; Damaševičius, R.; de Albuquerque, V.H.C. A Novel Transfer Learning
Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci. 2020, 10, 559.
https://doi.org/10.3390/app10020559
7. Amjad Khan; Zahid Ansari; Improved VGG-16 Convolutional Neural Network Based Lung Cancer Classification and Identification on Computed
Tomography
https://www.jncet.org/Manuscripts/Volume-11/Issue-2/Vol-11-issue-2-M-01.pdf
8. https://www.researchgate.net/publication/354228470_Lung-
GANs_Unsupervised_Representation_Learning_for_Lung_Disease_Classification_Using_Chest_CT_and_X-Ray_Images
20. DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 20
Name of the project/Thesis
Thank You!!!