Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Detection of chest pathologies using autocorrelation functionsIJECEIAES
An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
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.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
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.
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.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Detection of chest pathologies using autocorrelation functionsIJECEIAES
An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
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.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
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.
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.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
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.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
The document describes a deep learning framework for joint segmentation and classification of lung nodules in CT images. It uses a VGG19 network-assisted VGG-SegNet model for segmentation, extracting deep features from VGG19, and combining with handcrafted features for classification. The methodology involves collecting CT images, segmenting nodules using VGG-SegNet, extracting deep and handcrafted features, concatenating the features, and classifying images using various classifiers. Experimental results on LIDC-IDRI and Lung-PET-CT-Dx datasets show the proposed approach achieves 97.83% accuracy using an SVM-RBF classifier.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
USING DISTANCE MEASURE BASED CLASSIFICATION IN AUTOMATIC EXTRACTION OF LUNGS ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
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.
Cancer is a dangerous ailment that influences any part of the body and could produce malignant tumors. One feature of cancer is that abnormal cells create quickly and expand beyond their regular bounds. This could attack various parts of the human body and spread to other organs, which is the primary cause of cancer death. Cancer is becoming a more serious worldwide health concern. In the face of these threats, advanced technologies such as Artificial Intelligence (AI), cognitive systems, and the Internet of Things (IoT) may be insufficient to prevent, predict, diagnose, and treat cancer. Digital Twins (DT) with a combination of IoT, AI, cloud computing, and communications technologies such as 5G and 6G have the potential to significant reduce serious cancer threats. Observing data from DT populations may aid in the improvement of some cancer screening, prediction, prevention, detection, treatment, and research investment strategies. Applications of DT medicine specifically cancer, have been studied and analyzed in this paper using both conceptual and statistical analyses. This paper also shows a tree of some ailments where DT is applicable in their study. To the best of our knowledge, there is no literature research on various illnesses and DT specifically cancer disorders. To show the potential of DT, development hurdles of utilizing DT in cancer diseases are discussed, and then, several open research directions will be explained.
Coronavirus disease 2019 detection using deep features learningIJECEIAES
A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
MACHINE LEARNING MODEL FOR PNEUMONIA DETECTION FROM CHEST.pptxpadamsravan8
This document describes a machine learning model for pneumonia detection from chest X-ray images. Pneumonia is a serious respiratory infection that affects approximately 7% of the global population each year. The model aims to help diagnose pneumonia more rapidly and accurately through automated analysis of chest X-rays using deep learning techniques. This could lead to improved patient outcomes by facilitating earlier treatment interventions. The document reviews related work applying machine learning to chest X-ray analysis and pneumonia detection.
A deep learning approach for COVID-19 and pneumonia detection from chest X-r...IJECEIAES
There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
This document discusses detecting pneumonia in chest X-rays using deep learning techniques. It begins by stating that pneumonia is a major cause of death worldwide, especially in children. The objective is to develop a deep learning framework to automatically diagnose pneumonia from chest X-rays to reduce human error. Various deep learning models like CNN, VGG-16 and MobileNetV2 are implemented and compared on a public dataset. VGG-16 achieved the highest accuracy of 94.3% among the models for detecting pneumonia. The document concludes that pneumonia can be identified and classified using deep learning models with VGG-16 performing best.
Covid-19 Detection using Chest X-Ray ImagesIRJET Journal
1) The document discusses using deep learning and machine learning models to detect Covid-19 from chest x-ray images with a high accuracy.
2) Specifically, it evaluates using convolutional neural networks (CNNs) which are well-suited for medical image classification tasks since they can learn spatial relationships within images.
3) Previous studies that developed CNN and other models for Covid detection from chest x-rays are reviewed, finding classification accuracies from 87-99% depending on the dataset and model used.
ADVANCED HIERARCHICAL IMAGING TECHNIQUES IN TB DIAGNOSIS: LEVERAGING SWIN TRA...sipij
Lung Tuberculosis (TB) remains a critical health issue globally. Accurately detecting TB from chest x-rays
is vital for prompt diagnosis and treatment. Our study introduces an innovative approach using the swin
transformer to assist healthcare professionals in making faster, more accurate diagnoses. This method
also aims to lower diagnostic costs by streamlining the detection process. The swin transformer, a
sophisticated vision transformer, leverages hierarchical feature representation and a shifted window
mechanism for improved image Analysis.
Our research utilizes the nihchest x-ray dataset, comprising 1,557 non-tb and 3,498tb images. We divided
the dataset into training, validation, and testing sets in a 64%,16%, and 20% ratio, respectively. The
images undergo preprocessing—random resized crop, horizontal flip, and Normalization—before being
converted into tensors. We trained the swin transformer model over 50 epochs, with a batch size of 8,
using the adam optimizer at a learning rate of 1e-5. We closely monitored the model's accuracy and loss,
assessing its performance using metrics like the f1-score, precision, and recall.
Our findings show the model achieving a peak accuracy of 0.88 in the 43rd epoch for the training set, and
the same accuracy for the validation set after 20 epochs. During testing, we observed a precision of 0.7928
and 0.9008, recall of 0.7749 and 0.9099, and f1-scores of 0.7837 and 0.905 for the negative and positive
classes, respectively. The swin transformer demonstrates promising results, suggesting its adaptability and
potential in significantly enhancing diagnostic efficiency and accuracy in medical settings.
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.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
A Review Paper on Covid-19 Detection using Deep LearningIRJET Journal
This document reviews methods for detecting COVID-19 using deep learning techniques applied to chest X-rays and CT scans. It summarizes several research papers that have used convolutional neural networks and techniques like transfer learning to analyze medical images and accurately classify patients as COVID-19 positive or normal. The research shows these deep learning models can detect COVID-19 from images with high accuracy, even outperforming traditional PCR tests. Larger datasets are still needed to improve the models. Overall, the document concludes medical image analysis with deep learning is a promising approach for fast and effective COVID-19 detection.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
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.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
The document describes a deep learning framework for joint segmentation and classification of lung nodules in CT images. It uses a VGG19 network-assisted VGG-SegNet model for segmentation, extracting deep features from VGG19, and combining with handcrafted features for classification. The methodology involves collecting CT images, segmenting nodules using VGG-SegNet, extracting deep and handcrafted features, concatenating the features, and classifying images using various classifiers. Experimental results on LIDC-IDRI and Lung-PET-CT-Dx datasets show the proposed approach achieves 97.83% accuracy using an SVM-RBF classifier.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
USING DISTANCE MEASURE BASED CLASSIFICATION IN AUTOMATIC EXTRACTION OF LUNGS ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
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.
Cancer is a dangerous ailment that influences any part of the body and could produce malignant tumors. One feature of cancer is that abnormal cells create quickly and expand beyond their regular bounds. This could attack various parts of the human body and spread to other organs, which is the primary cause of cancer death. Cancer is becoming a more serious worldwide health concern. In the face of these threats, advanced technologies such as Artificial Intelligence (AI), cognitive systems, and the Internet of Things (IoT) may be insufficient to prevent, predict, diagnose, and treat cancer. Digital Twins (DT) with a combination of IoT, AI, cloud computing, and communications technologies such as 5G and 6G have the potential to significant reduce serious cancer threats. Observing data from DT populations may aid in the improvement of some cancer screening, prediction, prevention, detection, treatment, and research investment strategies. Applications of DT medicine specifically cancer, have been studied and analyzed in this paper using both conceptual and statistical analyses. This paper also shows a tree of some ailments where DT is applicable in their study. To the best of our knowledge, there is no literature research on various illnesses and DT specifically cancer disorders. To show the potential of DT, development hurdles of utilizing DT in cancer diseases are discussed, and then, several open research directions will be explained.
Coronavirus disease 2019 detection using deep features learningIJECEIAES
A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
MACHINE LEARNING MODEL FOR PNEUMONIA DETECTION FROM CHEST.pptxpadamsravan8
This document describes a machine learning model for pneumonia detection from chest X-ray images. Pneumonia is a serious respiratory infection that affects approximately 7% of the global population each year. The model aims to help diagnose pneumonia more rapidly and accurately through automated analysis of chest X-rays using deep learning techniques. This could lead to improved patient outcomes by facilitating earlier treatment interventions. The document reviews related work applying machine learning to chest X-ray analysis and pneumonia detection.
A deep learning approach for COVID-19 and pneumonia detection from chest X-r...IJECEIAES
There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
This document discusses detecting pneumonia in chest X-rays using deep learning techniques. It begins by stating that pneumonia is a major cause of death worldwide, especially in children. The objective is to develop a deep learning framework to automatically diagnose pneumonia from chest X-rays to reduce human error. Various deep learning models like CNN, VGG-16 and MobileNetV2 are implemented and compared on a public dataset. VGG-16 achieved the highest accuracy of 94.3% among the models for detecting pneumonia. The document concludes that pneumonia can be identified and classified using deep learning models with VGG-16 performing best.
Covid-19 Detection using Chest X-Ray ImagesIRJET Journal
1) The document discusses using deep learning and machine learning models to detect Covid-19 from chest x-ray images with a high accuracy.
2) Specifically, it evaluates using convolutional neural networks (CNNs) which are well-suited for medical image classification tasks since they can learn spatial relationships within images.
3) Previous studies that developed CNN and other models for Covid detection from chest x-rays are reviewed, finding classification accuracies from 87-99% depending on the dataset and model used.
ADVANCED HIERARCHICAL IMAGING TECHNIQUES IN TB DIAGNOSIS: LEVERAGING SWIN TRA...sipij
Lung Tuberculosis (TB) remains a critical health issue globally. Accurately detecting TB from chest x-rays
is vital for prompt diagnosis and treatment. Our study introduces an innovative approach using the swin
transformer to assist healthcare professionals in making faster, more accurate diagnoses. This method
also aims to lower diagnostic costs by streamlining the detection process. The swin transformer, a
sophisticated vision transformer, leverages hierarchical feature representation and a shifted window
mechanism for improved image Analysis.
Our research utilizes the nihchest x-ray dataset, comprising 1,557 non-tb and 3,498tb images. We divided
the dataset into training, validation, and testing sets in a 64%,16%, and 20% ratio, respectively. The
images undergo preprocessing—random resized crop, horizontal flip, and Normalization—before being
converted into tensors. We trained the swin transformer model over 50 epochs, with a batch size of 8,
using the adam optimizer at a learning rate of 1e-5. We closely monitored the model's accuracy and loss,
assessing its performance using metrics like the f1-score, precision, and recall.
Our findings show the model achieving a peak accuracy of 0.88 in the 43rd epoch for the training set, and
the same accuracy for the validation set after 20 epochs. During testing, we observed a precision of 0.7928
and 0.9008, recall of 0.7749 and 0.9099, and f1-scores of 0.7837 and 0.905 for the negative and positive
classes, respectively. The swin transformer demonstrates promising results, suggesting its adaptability and
potential in significantly enhancing diagnostic efficiency and accuracy in medical settings.
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.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
A Review Paper on Covid-19 Detection using Deep LearningIRJET Journal
This document reviews methods for detecting COVID-19 using deep learning techniques applied to chest X-rays and CT scans. It summarizes several research papers that have used convolutional neural networks and techniques like transfer learning to analyze medical images and accurately classify patients as COVID-19 positive or normal. The research shows these deep learning models can detect COVID-19 from images with high accuracy, even outperforming traditional PCR tests. Larger datasets are still needed to improve the models. Overall, the document concludes medical image analysis with deep learning is a promising approach for fast and effective COVID-19 detection.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
Practical eLearning Makeovers for EveryoneBianca Woods
Welcome to Practical eLearning Makeovers for Everyone. In this presentation, we’ll take a look at a bunch of easy-to-use visual design tips and tricks. And we’ll do this by using them to spruce up some eLearning screens that are in dire need of a new look.
Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
environmental standards.
Prepare feasibility studies, construction contracts, and tender documents with specifications and
tender analyses.
Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
meets environmental, safety, structural, zoning, and aesthetic standards.
Monitoring the progress of a project to assess whether or not it is in compliance with building plans
and project deadlines.
Attention to detail, exceptional time management, and strong problem-solving and communication
skills are required for this role.
Best Digital Marketing Strategy Build Your Online Presence 2024.pptxpavankumarpayexelsol
This presentation provides a comprehensive guide to the best digital marketing strategies for 2024, focusing on enhancing your online presence. Key topics include understanding and targeting your audience, building a user-friendly and mobile-responsive website, leveraging the power of social media platforms, optimizing content for search engines, and using email marketing to foster direct engagement. By adopting these strategies, you can increase brand visibility, drive traffic, generate leads, and ultimately boost sales, ensuring your business thrives in the competitive digital landscape.
Explore the essential graphic design tools and software that can elevate your creative projects. Discover industry favorites and innovative solutions for stunning design results.
3. Objective
• To conduct the automated detection of lung infections from computed
tomography (CT) images using Squeeze Net deep neural network
offers a great potential to augment the traditional healthcare strategy
for tackling COVID-19 and to measure performance metrics such as
accuracy, sensitivity and specificity
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 3
4. Abstract
• The coronavirus disease 2019 (COVID-19) has become a global pandemic since the beginning
of 2020.
• The disease has been regarded as a Public Health Emergency of International Concern (PHEIC)
by the World Health Organization (WHO) and the end of January 2020.
• Automated detection of lung infections from computed tomography (CT) images offers a great
potential to augment the traditional healthcare strategy for tackling COVID-19.
• However, segmenting infected regions from CT slices faces several challenges, including high
variation in infection characteristics, and low intensity contrast between infections and normal
tissues.
• Further, collecting a large amount of data is impractical within a short time period, inhibiting the
training of a deep model.
• To address these challenges, a novel COVID-19 Lung Infection Segmentation SqueezeNet
which is a convolutional neural network algorithm is proposed to automatically identify infected
regions from chest CT slices.
• In CNN, a parallel partial decoder is used to aggregate the high-level features and generate a
global map.
4
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
5. Introduction
• The coronavirus disease 2019 (COVID-19) has become a global
pandemic since the beginning of 2020
• Up to April 10, 2020, there have been more than 1.5 million cases of
COVID- 19 reported globally, with more than 92 thousands deaths
• The most common symptoms of COVID-19 patients include fever, cough
and shortness of breath, and the patients typically suffer from pneumonia.
• Computed Tomography (CT) imaging plays a vital role for detection of
manifestations in the lung associated with COVID-19 , where
segmentation of the infection lesions from CT scans is important for
quantitative measurement of the disease progression in accurate
diagnosis and follow-up assessment
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
5
6. Literature Survey
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHO
D
INFERENCE
1. “Diagnosis of Corona virus
disease 2019( covid-19) with
structured latent multi-view
representation learning”, IEEE
Transactions on Medical Imaging
Vol 39,no.8.
Feng Shi,
Changqing Zhang
and Dinggang Shen
Year: August,2020
Structured Latent Multi-
view Representation
Learning
Investigating
multiple features
describing CT
images from
different views, a
unified latent
representation is
learned which can
completely
encode
information from
different aspects
of features.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 6
7. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
2. “Lung infection quantification
of covid-19 in CT images with
deep learning”, IEEE
transactions on Computer
vision and Pattern
Recognition(cs.CV):Image
processing and quantitative
methods(q-bio.QM).
F.Shan et al,
Fei Shan, Yaozong
Gao, Yuxin Shi
Year: Mar 2020
Deep-learning(DL)-
based segmentation
system is developed to
automatically quantify
infection regions of
interest and their
volumetric ratios with
respect to lungs
For fast manual
delineation of
training samples
and possible
manual
intervention of
automatic results,
CT scans and
infection
distributions in the
lobes are correlated
well
7
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
8. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
3. “CPM-Nets: Cross partial multi-
view networks”, Dept: Advances
in Neural Information
Processing Systems 32(NeurIPS
2019)
Changquing Zhang,
Zongbo Han, Yajie
cui, Huazhu Fu
Year: 2019
Proposed a novel
framework termed
CPM-Nets, this
framework give a
formal definition of
completeness and
versatility for Multiview
representation
According to view-
missing patterns,
model fully
exploits all samples
and all views to
produce structured
representation for
interpretability
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 8
9. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
4. “Learning to segment skin
lesions from noisy annotations”,
IEEE based Computer Vision
and Pattern Recognition(cs.CV)
Ghassan
Hamarneh, Zahra
Mirikharaji, Yiqi Yan
Year: June 2019
Propose a spatially
adaptive reweighting
approach to treat clean
and noisy pixel-level
annotations
commensurately in the
loss function
Deploy a meta-
learning approach
to assign higher
importance to
pixels whose loss
gradient direction
is closer to those of
clean data
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 9
10. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
5. “Deep learning for chest
radiograph diagnosis: A
retrospective comparison of
the CheXNeXt algorithm to
practicing radiologists”,PLOS
Med.,vol .15 no.11
Pranav Rajpurkar,
Jeremy Irvin, Robyn
L.Ball, Hershel
Mehta
Year: Nov 2018
“Deep learning for chest
radiograph diagnosis: A
retrospective
comparison of the
CheXNeXt algorithm to
practicing radiologists”
“Deep learning for
chest radiograph
diagnosis: A
retrospective
comparison of the
CheXNeXt
algorithm to
practicing
radiologists”
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
10
11. Contd…
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
6. “Focal Dice loss and image
dilation for brain tumor
segmentation”, Lecture
Notes in Computer Science
book Series(LNCS, Vol
11045)
Pei Wang, Albert C,
S. Chung
Year: 20 Sep, 2018
Proposed a Focal Dice
Loss (FDL) method to
consider the imbalance
among structures of
interest instead of the
entire image including
background.
Image dilation
is applied to the
training
samples, which
enlarges the tiny
sub-regions,
bridges the
disconnected
pieces of tumor
structures and
promotes
understanding
on overall tumor
rather than
complex details
11
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
12. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
7. “Multi-view feature selection
and classification for
Alzheimer’s disease
diagnosis”, Multimedia tools
Appl .,Vol.76, no.8
M.Zhang, Y. Yang,
F.Shen, Y. Wang
Year: April 2017
Propose a novel multi-
view classification
method based on l2,p-
norm regularization for
Alzheimer’s disease
diagnosis
Investigated and
experimentally
demonstrated that
this method
enhances the
performance of
disease status
classification,
comparing to the
state-of-the-arts
methods
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 12
13. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
8. “Automatic detection and
classification of colorectal
polyps by transferring low-
level CNN features from
nonmedical domain”, IEEE J.
Biomedical. Health
Information,Vol.21, no.1
Ruikai Zhang, Yali
Zheng, Wing Chung
Year: Jan 2017
Propose a fully
automatic algorithm to
detect and classify
hyperplastic and
adenomatous polyps.
Proposed method
identified polyp
images from non-
polyp images in
the beginning
followed by
predicting the
polyp histology
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 13
14. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
9. “V-Net: Fully convolutional
neural networks for
volumetric medical image
segmentation”, in Proc.
Fourth Int. Conf .3D
Vis.(3DV).
F.Milletari,
N.Navab,and S.-
A.Ahmadi
Year: Oct 2016
Propose an approach to
3D image segmentation
based on a volumetric,
fully convolutional,
neural network
CNN is trained
end-to-end on MRI
volumes depicting
prostate, and learns
to predict
segmentation for
the whole volume
at once
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 14
15. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
10. “U-Net: Convolutional
networks for biomedical
image segmentation”, Dept:
Computer Vision and Pattern
Recognition (cs.CV)
O.Ronne berger,
P.Fischer, T.Brox
Year: May 2015
Present a network and
training strategy that
relies on the strong use
of data augmentation to
use the available
annotated samples more
efficiently.
The architecture
consists of a
contracting path to
capture context and
a symmetric
expanding path that
enables precise
localization
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 15
16. Contd….
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
11. “Visualizing data using t-SNE”,
J.Mach.Learn.Res., Vol .9,pp.
2579-2605
Laurens van der
Maaten, Geoffrey
Hinton
Year: 2008
t- SNE is that visualizes
high-dimensional data
by given each datapoint
in location in a two or
three dimensional
map,they are
significantly better than
those product by other
techniques on almost all
of the data sets.
For visualizing the
structure of very
large data sets, we
show how t-SNE
can use random
walks on
neighbourhood
graphs to allow the
implicit structure
of all of the data to
influence the way
in which a subset
of data is
displayed.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 16
17. Existing system
• ML-Machine Learning
• CNN-Convolutional Neural Network
• Deep Network (Inf-Net)
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 17
18. Machine Learning(ML)
• Machine learning is the study of computer algorithms that improve
automatically through experience and by the use of data. It is seen as a
part of artificial intelligence. ML approaches are divided into three
broad categories
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 18
19. Convolutional Neural Network(CNN)
• Convolutional Neural Network(CNN or ConvNet) is a class of deep
neural networks, most commonly applied to analysing visual imagery
• The term “Convolutional Neural Network” indicates that the network
indicates that the network employs a mathematical operation called
convolution. CNN is a type of artificial neural network used in image
recognition and processing that is specifically designed to process
pixel data.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 19
20. Deep Neural Network(Inf-Net)
• A Deep neural network (DNN) is an artificial neural
network (ANN) with multiple layers between the input and output
layers.
• DNNs are typically feedforward networks in which data flows from
the input layer to the output layer without looping back. At first, the
DNN creates a map of virtual neurons and assigns random numerical
values, or "weights", to connections between them. The weights and
inputs are multiplied and return an output between 0 and 1. If the
network did not accurately recognize a particular pattern, an algorithm
would adjust the weights.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 20
21. Proposed system
• A novel COVID-19 optimized Lung Infection Segmentation Deep
Network (Squeeze Net) is proposed to automatically identify infected
regions from chest CT slices.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 21
24. Input Image
• In this proposed model, we are giving CT images as an input to the preprocessing
block. Data set used to input are referred from “COVID-19 CT segmentation
dataset,” https://medicalsegmentation.com/covid19/, accessed: 2020-04-11.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 24
26. RGB to Gray:
An RGB image can be viewed as three images (a red scale image,a green
scale image and blue scale image) stacked on top of each other. In MATLAB, an
RGB image is basically a M*N*3 array of color pixel, Where each color pixel is a
triplet which corresponds to red, blue and green color component of RGB image at a
specified spatial location. Similarly, A Gray scale image can be viewed as a single
layer image In MATLAB, a Gray scale image is basically M*N array whose values
have been scaled to represent intensities.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 26
27. Image Resizing:
• Image Resizing is necessary when we need to increase/ decrease the
total number of pixels, whereas remapping can occur when we are
correcting for lens distortion or rotating an image. As, Neural
networks receive inputs of the same size, all images need to be resized
to a pixel size before inputting them to the CNN.
27
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
28. Filter
• Median filtering is used to remove the noises present in images
• The Median filter is a non-linear digital filtering technique, often used
to remove noise from an image or signal. Such noise reduction is a
typical pre-processing (for eg: Edge detection on an image).
28
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
29. Feature extraction
The key concept of the low and high level features extracted from input
images. Low level features include edges and blobs, and high level
features include objects and events. Low level feature extraction is
based on signal/image processing techniques. While the high level
feature extraction is based on machine learning techniques.
29
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
30. SqueezeNet
What is SqueezeNet?
a deep convolutional neural network (CNN)
compressed architecture design
model contains relatively small amount of parameters
achieve AlexNet-level accuracy on ImageNet dataset with 50x fewer
parameters
Three advantages of small CNN architectures:
require less communication across servers during distributed training.
require less bandwidth to export a new model from the cloud.
more feasible to deploy on customized hardware with limited memory.
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 30
31. SqueezeNet
Conventional 3x3 rfilter or other replaced by 1x1 convolution filters
1x1 filter has 9X fewer parameters than a 3x3 filter
Fewer inputs to conv layers result in fewer parameters achieved by
using only 1x1 filters prior to the 3x3 conv layer called the squeeze
layer (description in next section)total number of parameters in 3x3
conv layer = (number of input channels) (number of filters) (3*3)
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 31
33. SqueezeNet Architecture
Layers breakdown
layer 1: regular convolution layer
layer 2-9: fire module (squeeze + expand layer)
layer 10: regular convolution layer
layer 11: softmax layer
Architecure specifications
gradually increase number of filters per fire module
max-pooling with stride of 2 after layer 1,4,8
average-pooling after layer 10
delayed downsampling with pooling layers
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
33
34. What is the Fire Module?
• building block used in the
SqueezeNet
• Employs Strategies 1, 2, and 3
• Comprised of squeeze layers which
have only 1x1 filters (strategy 1)
• Comprised of expand layers which
have a mix of 1x1 and 3x3
convolution filters
• Number of filters in squeeze layer
must be less than the expand layer
(strategy 2)
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 34
35. ResNet
• ResNet - Residual Networks is a classic neural network used as a
backbone for many computer vision tasks. This model was the winner
of ImageNet challenge in 2015. The fundamental breakthrough
with ResNet was it allowed us to train extremely deep neural networks
with 150+layers successfully. The basic block diagram of Residual
block is shown as below
35
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
36. Hardware/Software Requirements
SOFTWARE REQUIRED: MATLAB 2014 a
MATLAB supports standard data and image formats, including JPEG, JPEG-2000,
TIFF, PNG, HDF, HDF-EOS, FITS, Microsoft® Excel®, ASCII, and binary files. It also supports the
multiband image formats BIP and BIL, as used by LANDSAT for example. Low-level I/O and
memory mapping functions enable you to develop custom routines for working with any data format.
HARDWARE REQUIRED:
• System : Windows 10 Pro
• Processor : Up to 2.13 GHz
• Memory : Up to 512 MB RAM.
36
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
49. Advantages
• Higher accuracy
• Less training needed
• High Dimensionality
• Better Specificity and Sensitivity
49
K.L.N.C.I.T /ECE Viva Voce 9/8/2021
50. Conclusion
• Deep learning practices are an area where high scientific achievements are
obtained in different scientific fields day by day. One of these fields is medical
practices and studies such as disease detection, disease classification, and location
of the disease are carried out.
• Dataset were performed as input data to the Squeeze Net network using image
processing techniques. The network, achieved higher accuracy. Squeeze Net
structure, which has been used less than other popular deep learning methods in
previous studies, combined with image processing methods, has shown a
successful result.
• In this proposed model, we increased the parameters such as sensitivity, specificity
and accuracy with 0.5000,0.5000, 99.7%. The ultimate aim of this paper is to
increase the success rate rather than referred paper and Thus, it has shown a
successful result. In this proposed model , we ought to train less models for the
faster execution purpose. Apart all these things, our proposed model accuracy is
remarkable.
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51. References
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