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.
Chest X-ray Pneumonia Classification with Deep LearningBaoTramDuong2
This document discusses using deep learning models to classify chest x-ray images as either normal or pneumonia. It obtained a dataset of over 5,800 pediatric chest x-rays from a Chinese hospital. Various deep learning models were explored, including multilayer perceptrons, convolutional neural networks, and transfer learning with VGG16, which achieved 92% validation accuracy. The document recommends future work such as distinguishing between viral and bacterial pneumonia and combining models with SVM. It also discusses recommendations to reduce childhood pneumonia prevalence.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
This document describes a system for detecting brain tumors in MRI images using image segmentation. It discusses how existing manual detection of tumors is difficult due to noise and requires many days. The proposed system applies preprocessing like filtering and grayscale conversion. It then uses image segmentation techniques to detect tumor edges and boundaries. Features are extracted and classification is used to differentiate between normal and tumor images, helping doctors detect tumors earlier. The system is implemented in MATLAB and aims to overcome difficulties in early tumor detection.
Chest X-ray Pneumonia Classification with Deep LearningBaoTramDuong2
This document discusses using deep learning models to classify chest x-ray images as either normal or pneumonia. It obtained a dataset of over 5,800 pediatric chest x-rays from a Chinese hospital. Various deep learning models were explored, including multilayer perceptrons, convolutional neural networks, and transfer learning with VGG16, which achieved 92% validation accuracy. The document recommends future work such as distinguishing between viral and bacterial pneumonia and combining models with SVM. It also discusses recommendations to reduce childhood pneumonia prevalence.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
This document describes a system for detecting brain tumors in MRI images using image segmentation. It discusses how existing manual detection of tumors is difficult due to noise and requires many days. The proposed system applies preprocessing like filtering and grayscale conversion. It then uses image segmentation techniques to detect tumor edges and boundaries. Features are extracted and classification is used to differentiate between normal and tumor images, helping doctors detect tumors earlier. The system is implemented in MATLAB and aims to overcome difficulties in early tumor detection.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION khanam22
The document presents three methods for tumor detection in MRI images: 1) K-means clustering with watershed algorithm, 2) Optimized K-means using genetic algorithm, and 3) Optimized C-means using genetic algorithm. It evaluates each method, finding that C-means clustering with genetic algorithm most accurately detects tumors by assigning data points to multiple clusters and finding the optimal solution in less time. The proposed approach successfully detects tumors with high accuracy, identifies the tumor area and internal structure, and provides a colorized output image.
Sign Language Recognition based on Hands symbols ClassificationTriloki Gupta
Communication is always having a great impact in every domain and how it is considered the meaning of the thoughts and expressions that attract the researchers to bridge this gap for every living being.
The objective of this project is to identify the symbolic expression through images so that the communication gap between a normal and hearing impaired person can be easily bridged.
Github Link:https://github.com/TrilokiDA/Hand_Sign_Language
The document discusses using deep learning approaches for handwritten Bangla digit recognition. It proposes using convolutional neural networks (CNNs) and deep belief networks (DBNs) with dropout and different filters. It finds that a CNN using Gabor features and dropout achieved the best accuracy compared to other techniques. The research is continuing to develop more advanced neural networks combining state preserving extreme learning machines to recognize Bangla numerals and characters.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
This document describes a project on age and gender detection using deep learning and convolutional neural networks. The objectives are to pretrain a model using the UTKFace dataset to detect age and gender from facial images. The methodology involves data preprocessing like grayscale conversion, resizing, normalization. A CNN model is built with convolutional blocks and fully connected layers. The model is compiled with binary cross entropy loss for gender classification and mean absolute error for age detection. The trained model is tested and deployed using streamlit. Real-world use cases discussed are advertising based on targeted audiences and an Android app that detects age from photos.
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Since its launch in mid-January, the Data Science Bowl Lung Cancer Detection Competition has attracted more than 1,000 submissions. To be successful in this competition, data scientists need to be able to get started quickly and make rapid iterative changes. In this talk, we show how to compute features of the scanned images in the competition with a pre-trained Convolutional Neural Network (CNN) with Cognitive Toolkit (previously named CNTK), and use these features to classify the scans into cancerous or not cancerous, using a boosted tree with Light-GBM library, all in one hour.
Blog post: https://blogs.technet.microsoft.com/machinelearning/2017/02/17/quick-start-guide-to-the-data-science-bowl-lung-cancer-detection-challenge-using-deep-learning-microsoft-cognitive-toolkit-and-azure-gpu-vms/
Artificial Neural Network seminar presentation using ppt.Mohd Faiz
- Artificial neural networks are inspired by biological neural networks and learning processes. They attempt to mimic the workings of the brain using simple units called artificial neurons that are connected in networks.
- Learning in neural networks involves modifying the synaptic strengths between neurons through mathematical optimization techniques. The goal is to minimize an error function that measures how well the network can approximate or complete a task.
- Neural networks can learn complex nonlinear functions through training algorithms like backpropagation that determine how to adjust the synaptic weights to improve performance on the learning task.
The document discusses Automatic Number Plate Recognition (ANPR) systems. It provides the following key points:
1. ANPR uses optical character recognition on images captured by specialized cameras to read license plates on vehicles.
2. The cameras capture images that are then processed by ANPR software to detect, segment, and identify the license plate numbers.
3. ANPR systems are commonly used for electronic toll collection, traffic management, parking enforcement, and border control by storing images and license plate data.
This document provides an overview of a license plate recognition system. It introduces ALPR cameras and their benefits like being cost effective, requiring no human resources, and providing higher accuracy. It then describes how the system works using Matlab, C++, Processing, and Arduino to extract the license plate number from a video, recognize the characters, look up the owner's phone number, and send an SMS notification. The document includes sections on the introduction, why ALPR cameras, how it works, problems faced, and a conclusion.
This document summarizes the design and implementation of a child tracking device using GPS, GSM, and Arduino. The system aims to ensure children's safety by allowing parents to track their location through SMS. It consists of three main subsystems - a GPS module to obtain location coordinates, a GSM module to send coordinates via SMS, and an Arduino board to connect the components. The design was implemented on a PCB board with low-cost components. Experimental results showed the system could successfully send location updates to parents' phones via SMS and display the location on a map. The system provides a low-cost solution for tracking children and reducing missing child cases.
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
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.
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.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION khanam22
The document presents three methods for tumor detection in MRI images: 1) K-means clustering with watershed algorithm, 2) Optimized K-means using genetic algorithm, and 3) Optimized C-means using genetic algorithm. It evaluates each method, finding that C-means clustering with genetic algorithm most accurately detects tumors by assigning data points to multiple clusters and finding the optimal solution in less time. The proposed approach successfully detects tumors with high accuracy, identifies the tumor area and internal structure, and provides a colorized output image.
Sign Language Recognition based on Hands symbols ClassificationTriloki Gupta
Communication is always having a great impact in every domain and how it is considered the meaning of the thoughts and expressions that attract the researchers to bridge this gap for every living being.
The objective of this project is to identify the symbolic expression through images so that the communication gap between a normal and hearing impaired person can be easily bridged.
Github Link:https://github.com/TrilokiDA/Hand_Sign_Language
The document discusses using deep learning approaches for handwritten Bangla digit recognition. It proposes using convolutional neural networks (CNNs) and deep belief networks (DBNs) with dropout and different filters. It finds that a CNN using Gabor features and dropout achieved the best accuracy compared to other techniques. The research is continuing to develop more advanced neural networks combining state preserving extreme learning machines to recognize Bangla numerals and characters.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
This document describes a project on age and gender detection using deep learning and convolutional neural networks. The objectives are to pretrain a model using the UTKFace dataset to detect age and gender from facial images. The methodology involves data preprocessing like grayscale conversion, resizing, normalization. A CNN model is built with convolutional blocks and fully connected layers. The model is compiled with binary cross entropy loss for gender classification and mean absolute error for age detection. The trained model is tested and deployed using streamlit. Real-world use cases discussed are advertising based on targeted audiences and an Android app that detects age from photos.
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Since its launch in mid-January, the Data Science Bowl Lung Cancer Detection Competition has attracted more than 1,000 submissions. To be successful in this competition, data scientists need to be able to get started quickly and make rapid iterative changes. In this talk, we show how to compute features of the scanned images in the competition with a pre-trained Convolutional Neural Network (CNN) with Cognitive Toolkit (previously named CNTK), and use these features to classify the scans into cancerous or not cancerous, using a boosted tree with Light-GBM library, all in one hour.
Blog post: https://blogs.technet.microsoft.com/machinelearning/2017/02/17/quick-start-guide-to-the-data-science-bowl-lung-cancer-detection-challenge-using-deep-learning-microsoft-cognitive-toolkit-and-azure-gpu-vms/
Artificial Neural Network seminar presentation using ppt.Mohd Faiz
- Artificial neural networks are inspired by biological neural networks and learning processes. They attempt to mimic the workings of the brain using simple units called artificial neurons that are connected in networks.
- Learning in neural networks involves modifying the synaptic strengths between neurons through mathematical optimization techniques. The goal is to minimize an error function that measures how well the network can approximate or complete a task.
- Neural networks can learn complex nonlinear functions through training algorithms like backpropagation that determine how to adjust the synaptic weights to improve performance on the learning task.
The document discusses Automatic Number Plate Recognition (ANPR) systems. It provides the following key points:
1. ANPR uses optical character recognition on images captured by specialized cameras to read license plates on vehicles.
2. The cameras capture images that are then processed by ANPR software to detect, segment, and identify the license plate numbers.
3. ANPR systems are commonly used for electronic toll collection, traffic management, parking enforcement, and border control by storing images and license plate data.
This document provides an overview of a license plate recognition system. It introduces ALPR cameras and their benefits like being cost effective, requiring no human resources, and providing higher accuracy. It then describes how the system works using Matlab, C++, Processing, and Arduino to extract the license plate number from a video, recognize the characters, look up the owner's phone number, and send an SMS notification. The document includes sections on the introduction, why ALPR cameras, how it works, problems faced, and a conclusion.
This document summarizes the design and implementation of a child tracking device using GPS, GSM, and Arduino. The system aims to ensure children's safety by allowing parents to track their location through SMS. It consists of three main subsystems - a GPS module to obtain location coordinates, a GSM module to send coordinates via SMS, and an Arduino board to connect the components. The design was implemented on a PCB board with low-cost components. Experimental results showed the system could successfully send location updates to parents' phones via SMS and display the location on a map. The system provides a low-cost solution for tracking children and reducing missing child cases.
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
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.
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.
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 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.
Pneumonia prediction on chest x-ray images using deep learning approachIAESIJAI
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients.
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.
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.
This document summarizes a study that used machine learning and deep learning algorithms like support vector regression, polynomial regression, deep neural networks, and recurrent neural networks with long short-term memory to analyze the COVID-19 epidemic. The models were trained on real-time data from the Johns Hopkins dashboard to predict confirmed, recovered, and death cases worldwide and analyze the daily transmission behavior of the virus. The polynomial regression model yielded the lowest error in forecasting COVID-19 transmission compared to other approaches.
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.
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.
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%.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
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.
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.
An intelligent approach for detection of covid by analysing Xray and CT scan ...IRJET Journal
This document presents an intelligent approach to detect COVID-19 using X-ray and CT scan images. It discusses developing a deep learning model using a convolutional neural network (CNN) to analyze medical images and classify them as coming from COVID-19 positive or negative cases. The model would be integrated into a web application using Flask that allows users to upload images for rapid diagnosis. The goal is to address issues with traditional testing methods that can take days to get results, and to help reduce the spread of COVID-19 through faster detection. The document reviews several related studies applying deep learning to COVID-19 detection from medical images and discusses the materials and methodology used to develop and evaluate the proposed intelligent detection system.
IRJET - Detecting Pneumonia from Chest X-Ray Images using Committee MachineIRJET Journal
This document discusses detecting pneumonia from chest X-ray images using a committee machine. It aims to build a web application that can accurately diagnose pneumonia by analyzing chest X-ray images. The system will use a dataset of over 5,800 chest X-ray images to predict if an image shows pneumonia. It will apply techniques like image processing, machine learning algorithms, and a committee machine to increase the accuracy of pneumonia detection compared to other methods.
Deep Learning for Pneumonia Diagnosis: A Comprehensive Analysis of CNN and Tr...IRJET Journal
This document summarizes a research paper that proposes using convolutional neural networks and transfer learning to accurately diagnose pneumonia from chest x-rays. The paper describes how pneumonia affects the lungs and the importance of early detection. It discusses how CNNs and transfer learning have been successfully used for medical image classification. The proposed model uses pre-trained CNN architectures like MobileNet, Inception, ResNet and EfficientNet applied to a dataset of chest x-rays to distinguish between normal and pneumonia cases. The model achieves highly accurate pneumonia detection, which could help improve patient outcomes.
An approach of cervical cancer diagnosis using class weighting and oversampli...TELKOMNIKA JOURNAL
Globally, cervical cancer caused 604,127 new cases and 341,831 deaths in 2020, according to the global cancer observatory. In addition, the number of cervical cancer patients who have no symptoms has grown recently. Therefore, giving patients early notice of the possibility of cervical cancer is a useful task since it would enable them to have a clear understanding of their health state. The use of artificial intelligence (AI), particularly in machine learning, in this work is continually uncovering cervical cancer. With the help of a logit model and a new deep learning technique, we hope to identify cervical cancer using patient-provided data. For better outcomes, we employ Keras deep learning and its technique, which includes class weighting and oversampling. In comparison to the actual diagnostic result, the experimental result with model accuracy is 94.18%, and it also demonstrates a successful logit model cervical cancer prediction.
AN AUTOMATED FRAMEWORK FOR DIAGNOSING LUNGS RELATED ISSUES USING ML AND DATA ...IRJET Journal
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1. Classification of Pneumonia from chest
X-rays using Transfer Learning
MSc Research Project
MSc in Data Analytics
Tushar Shailesh Dalvi
Student ID: x18134301
School of Computing
National College of Ireland
Supervisor: Mr. Noel Cosgrave
2. National College of Ireland
Project Submission Sheet
School of Computing
Student Name: Tushar Shailesh Dalvi
Student ID: x18134301
Programme: MSc in Data Analytics
Year: 2019
Module: MSc Research Project
Supervisor: Mr. Noel Cosgrave
Submission Due Date: 12/12/2019
Project Title: Classification of Pneumonia from chest X-rays using Transfer
Learning
Word Count: 6567
Page Count: 20
I hereby certify that the information contained in this (my submission) is information
pertaining to research I conducted for this project. All information other than my own
contribution will be fully referenced and listed in the relevant bibliography section at the
rear of the project.
ALL internet material must be referenced in the bibliography section. Students are
required to use the Referencing Standard specified in the report template. To use other
author’s written or electronic work is illegal (plagiarism) and may result in disciplinary
action.
Signature:
Date: 11th December 2019
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Attach a completed copy of this sheet to each project (including multiple copies).
Attach a Moodle submission receipt of the online project submission, to
each project (including multiple copies).
You must ensure that you retain a HARD COPY of the project, both for
your own reference and in case a project is lost or mislaid. It is not sufficient to keep
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3. Classification of Pneumonia from chest X-rays using
Transfer Learning
Tushar Shailesh Dalvi
x18134301
Abstract
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, Ho-
rizontal Flip, width shift and height shift techniques used to complete the aug-
mentation 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 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.
1 Introduction
Pneumonia is one of the common diseases throughout human history, it is derived from
the Greek word (pne´um¯on) meaning ”lung”. Cough, fever, breathing difficulties are the
symptoms of pneumonia which are Initially described by the Hippocrates in 460-370 BC.
Pneumonia is more common in old and very young people because they have weaker
lungs. Even mild viral pneumonia also can be life-threatening in those peoples whose
immune systems are weak. Pneumonia primarily caused by bacteria and viruses and less
commonly by fungi and parasites. Pneumonia usually begins as a higher respiration tract
contamination that movements into the lower breathing tract. In this, the alveoli (air
sac) which is inside the lungs are filled with the fluid, which makes harder for the lungs
to pass oxygen into bloodstream. As per the national vital statistics report Pneumonia is
on 8th rank among 10th leading death cause (Heron; 2018). In 2016, 51537 death caused
by pneumonia in Alone USA. And according to World Health Organization 15% of all
deaths of children under 15 years old are caused by pneumonia, which means Pneumonia
killed 808694 children in 2017. In the term of antibiotic treatment to treat pneumonia,
it took around US$ 109 million per year. Of course, the advantage of this treatment can
only take those patients who knows they are affected with pneumonia in early stages.
It takes lots of efforts and manpower to detect pneumonia in patients. Day by day for
pneumonia patients getting hard to cure the diseases, and for that we need experienced
and well-trained doctors so they can identify the pneumonia and help to cure the illness.
1
4. Identifying pneumonia from a chest x-ray is one of the famous techniques developed
by doctors, in which fluid is identified in lungs. It is hard for any layman to find fluid in
lungs and workload on specially trained practiser increase the risk of pneumonia infected
people. To reduce this risk, we need to develop a computer-based system that can identify
pneumonia from a chest x-ray. Currently, very few systems can identify the organs and
tissue in the medical image analysis, and still makes an issue for implementing AI-based
Solution. this research is conducted to develop a system that can identify pneumonia from
a chest x-ray, also the primary goal to achieve high recall on classification of disease. In
this research custom Vgg19 model developed to carry out this research.
Hence From the above discussion, the research question will be:
Can the custom VGG19 model help to improve the recall of pneumonia
detection from chest X-ray images as compare state of the art technology?
In this study, we will use the CNN based model which will train for the image clas-
sification process. In section 2, we will see the detail description of previously done
researches and find out the best possible technique for classification technique. In section
3 explained the detailed methodology with subsection like Business Requirement, setup,
Data understanding, modelling, and In section 4 overall flow of research is explained. sec-
tion 5 represented proper implementation method. After that section 6 provides insight
of our model performance and in next section 7 result will be discussed. finally in section
8 conclusion and future work is explained.
2 Related Work
In the Field of Pneumonia classification, so many researchers contributed their work to
take motivation. Everyone used different techniques, different methodology and different
datasets to get state of the art output in same field. most of the Researchers used CNN
based Models on different datasets, hence we dividing the literature review into sections
with Different available datasets.
2.0.1 RSNA Dataset:
Researcher Liang and Zheng (2019) performed pneumonia detection on Chest X-ray data-
set which contained a total of 5856 X-ray images of pneumonia and normal as well, also
the number of images of normal as compare pneumonia was low, the researcher used im-
age augmentation to reduce the overfitting. The researcher used CNN techniques in which
developed their own CNN Architecture with 49 convolutional Layers and 2 dense layers
researcher used residual network-based models to differentiate the actual observed values
and estimated values this helps the model to learn on small changes. In this research, the
researcher used other models like VGG16 (Simonyan and Zisserman; 2015), Densnet121,
Xception and inception V3 used to compare the result with the custom model. The result
of this research got the highest recall and precision from custom model, 0.967 and 0.891
respectively as compare with another algorithm.
In previously researcher Liang and Zheng (2019) developed its own CNN architecture
with 49 Convolutional layers and 2 dense layers for pneumonia classification but another
researcher O’Quinn et al. (2019) used the Alexnet network on the same the RSNA dataset.
Alexnet network created to classify the 1000 different classes, which consist of 650,000
2
5. neurons and 60 million parameters including 5 convolutional layers followed by max-
pooling layer and 3 fully connected layers with 1000-way SoftMax. To carried out this
research, the researcher used the fine-tune pre-trained model using transfer learning and
replaced the last 3 layers replaced by 3 fully connected layers for classifying two categories.
For further execution, this program researcher divided data in the ratio of 70-30%, in
which 70% used for training purposes and remaining used as validation dataset. With the
7922 training images researcher set batch size of 128 images for 20 epochs it took around
62 iterations to complete single epoch. With this setting Researcher able to achieve 72%
initial accuracy on validation dataset which used separately. In conclusion, researcher
proposed the image augmentation on dataset to improve the result. The same approach
used in another research but with image augmentation researcher Stephen et al. (2019)
developed own network with convolution, max pooling, and classification layers combined
together. The researcher created several dataset sizes to check different approaches in
which Researcher able to achieve highest of 95% training accuracy and 93.73% accuracy
on validation dataset which can show us the importance of image augmentation.
In spite of the previous two researchers, this researcher used a different approach,
researcher Islam et al. (2019) used compressed Sensing based on deep learning framework
for pneumonia detection from X-ray images. In this paper, the researcher used a simple
multilayer network with 4 convolutional and 3 fully connected layers as hidden layers also
for non-linearity ReLU (Rectified Linear Unit) activation function used. The whole data-
set was distributed in 70%, 25%, 5% ratio for training, validation and testing purpose.
The researcher used Peak Signal to Noise Ratio (PSNR) and mean Structural SIMilarity
(SSIM) measures to calculate the reconstructed X-Ray images. The user runs this CNN
model for 500 epochs and after 400 epochs model achieved 98.82% and 99.80% accur-
acy on training and validation. Throughout this whole research researcher used 3 more
methods to compare against Proposed Method, in which F-cMeans got the least predic-
tion accuracy 85.87%, another method DL (Deep Learning) and ChexNet algorithm got
91.16% and 95.28% accuracy, and the proposed model able to achieve 97.34% Prediction
Accuracy.
According to Sirazitdinov et al. (2019) ensemble method is also becoming handy for
pneumonia classification hence researcher used an ensemble of RetinaNet and Mask R-
CNN to detect pneumonia from chest X-ray. Initially, the base network trained with the
COCO network and use image augmentation like Random Brightness, random degree
rotation. In the training phase both models trained to predict the pneumonia region
then NonMaximum Suppression (NMS) was used to set threshold in predicted region.
Afterword depending on similarity of pneumonia region model getting selected and if
both models have different areas of region in that case ensemble method uses to select
area with maximum confidence. With this method Sirazitdinov et al. (2019) got 0.790
accuracy for pneumonia prediction. The usefulness for ensemble method is further stated
in the research which is published by Ko et al. (2019) with the help of RetinaNet and
Mask R-CNN networks ensemble model created and for the accuracy measurement mean
average precision (mAP) used. To improve the mAP models trained individually and
adjusted ratio of weight for more accuracy. This process helps to achieve the highest
mAP of 0.21746. Jaiswal et al. (2019) used same Mask RCNN method with little changes
initially set the threshold on ResNet50 and ResNet101 to get optimum results, and both
models combined as ensemble method to get maximum prediction value. In this research
image augmentation, dropout and L2 Regularization used to reduce overfitting but that
cause weaker results on training set as compare testing dataset.
3
6. 2.0.2 ChexNet:
In the year 2018, Jaipurkar et al. (2018) used DensNet based model for classification of
Chest X-ray dataset with 13 more diseases including pneumonia, the main purpose of this
model is to reduce loss and reuse of features. One of the key characteristics of DensNet
is it creates a shorter connection from the first layer and that helps to reuse the features.
The creating of bounding boxes on the sample dataset helps to achieve more accuracy, F-
Score, recall and precision. Chest X-ray data was having class imbalance to reduce class
imbalance data augmentation is used like Stephen et al. (2019) and Sirazitdinov et al.
(2019), also initial layers trained on ImageNet dataset to identify curves and boundaries.
All these techniques helped to achieve the accuracy of the validation dataset as compare to
CheXnet network, proposed network got a high accuracy of 76% and 96% for pneumonia
disease.
Using deep convolutional Neural network gives advantages for feature selection and
data training process it helps to make task easy but, every network have its own feature
selection techniques and different numbers of layers Researcher Islam et al. (2017) used
six different neural that is Alexnet, VGG16, VGG 19, ResNet-50, ResNet-101, ResNet-
152 to get the best result. and on the top of that also used ensemble method to compare
the result with pre-trained models. This research helps to find out how each network is
different from each other, throughout this research ensemble method able to achieve the
highest accuracy with 94% but in terms of sensitivity VGG19 network overpowered other
networks and in the context of specificity Alexnet got the highest accuracy of 93%. In
another research Rahmat et al. (2019) used Faster Regional Convolutional Neural network
(Fast R-CNN) for detection in which Reginal proposal Network predicts the object bound
and score and also can be used as fully convolutional network, to handle loss three different
loss function used which helps to handle total loss and classification loss, throughout this
result researcher able identify pneumonia faster than general practitioner and medical
student, with 62% accuracy.
With the use of CNN Abiyev and Ma’aitah (2018) introduced to another method
of pneumonia detection using supervised and unsupervised learning, the researcher used
Back Propagation neural network with supervised learning and Competitive neural net-
work with unsupervised learning, also used CNN for comparative purpose. The result is
discussed in terms of accuracy, error rate and training time between networks. Multilayer
feedforward neural network used in BPNN as an error BackPropagation algorithm which
consists of input hidden and output layer in the network which helps to repeat back-
ward and forward computation for training pairs. In CpNN only the Input and Output
layer are fully connected to each other. the main reason to use this algorithm because
these networks show efficiency in computer vision and biological computation. The third
network is used as CNN which consists of a convolutional layer, fully connected layers
and sub-sampling layer for activation function ReLU and sigmoid used. After testing all
network CNN outperformed with the highest accuracy rate of 92% followed by CpNN
and BPNN with 89.57% and 80.04% respectively. Abiyev and Ma’aitah (2018) also used
VGG 16 and VGG19 network and able to achieve 86% and 92% accuracy rate. For further
study researcher suggested using VGG16 and VGG19 network on different large datasets
to get the highest result.
Initially, CNN was helped in image classification with start to end approach but now
researchers finding different ways of utilizing CNN for the same purpose. Heo et al.
(2019) used 1000 images of pneumonia infected people and another 1000 images of non-
4
7. pneumonia patients. like previous researcher Abiyev and Ma’aitah (2018) VGG19, Incep-
tionV3, ResNet50 used for feature extraction also in addition age, height and gender used
as a demographic variable. But in images there are other structures like spine and heart
are included, to reduce this structure researcher generated mask which is included lung
area only to generate this mask U-Net algorithm used, on the basis of this masked out-
put remaining model got trained, in training researcher used different CNN algorithm on
both image-based demographic-based dataset. In the testing phase, image-based model
outperformed on a demographics-based model in terms of AOC, but combination of both
provided more efficient results. In which a combination of images and all demographic
variables outperformed different combinations with 0.9213 in the scale of AUC.
Till now we saw so many used CNN model approaches in which well-known algorithms
are used to get the highest output but still some algorithms like Multilayer extreme
learning machine (MLELM) and Online Sequential Extreme Learning Machine (OSELM)
rarely used in the researches. Vijendran and Dubey (2019) used both algorithms and the
ensemble of these algorithms are used for pneumonia classification. MLELM and OSELM
both consist of an input layer hidden layer and output layer. Training accuracy for both
models was 91.1% and 95% respectively. but an ensemble of MLELM and OSELM which
was known as Multilayer OS-ELM outperformed them with 96% accuracy. Even on the
testing dataset Multilayer OS-ELM outperformed with 91.7%, which helps to recognize
the less known algorithms are also useable for image classification.
Researcher Liu et al. (2019) used a novel technique called Segmentation Based Fusion
Network (SDFN) in this research two CNN based classification models were used as
feature extractors and the Lung region generator used to identify the lung regions. for
the CNN model Fine-tuned Densnet is used like Pan et al. (2019) and Heo et al. (2019),
and in the last FC layer, the sigmoid activation function used to normalization. With
this technique approximately 95.84% of lung region detected and 0.719 AUC Score obtain
for pneumonia detection.
In image classification or detection parameters like image size and noise in image
always plays a crucial task in result, Sharma et al. (2018) completed research to identify
how the quality and size of the image effect on image classification result. The researcher
uses a different size of images with different noise levels to justify the research. Mainly
chest X-ray images are used in this study. The researcher took different sizes of images
with image resolution from 2048x2048 to 500x500 and performed the Otsu thresholding
method to identify the pneumonia clouds from pneumonia affected patients. Totally 40
images used for this process and studied that the images with high resolution having
high chances to detect pneumonia as compare to low images with a low resolution like
500x500.
2.0.3 Mendeley Dataset:
As we see in previous researches researcher Islam et al. (2017), Liang and Zheng (2019)
and Jaipurkar et al. (2018) used the DensNet, VGG16, Inception and ResNet50/101/152
models to classify pneumonia with state of the art techniques using multiple algorithms
not only provide broad view about respective networks but also helps to choose the
perfect model for solving a problem. Furthermore, Ayan and ¨Unver (2019) used VGG16
against the Xception algorithm, which not used throughout the lit review. While using
the Xception model pre-trained ImageNet weights used for initial layers also researcher
added 2 fully connected layers with two-way output layers with a SoftMax activation
5
8. function. This model trained on more than 5000 images which included both normal and
pneumonia images while testing on test dataset VGG16 model outperformed in accuracy
and specificity against Xception with 0.87 and 0.91 scores but Xception able to achieve
the highest sensitivity with 0.85 and recall was 0.94.
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bination, an ensemble of classifier generated. The self-labeled classifier used by applying
different algorithms and every prediction of algorithm are selected through voting meth-
odology.in the training phase dataset is labeled with L and U notation. After that voting
fusion techniques used for the final hypothesis and the output are measured by sensitivity,
specificity, and F-Measure.
2.0.4 Other Dataset:
Researcher Taylor et al. (2018) used VGG 19, Xception and inception models to carry
pneumonia detection quests Islam et al. (2017), with the dataset with 13292 images
including Pneumonia and normal cases. In which the researcher split data into 70:15:15
ratio for training, testing and validation, after running all this model researcher able
to achieve 0.79 sensitivity for the VGG19 model and the Inception model is measured
on specificity with 0.97. with the same VGG16 Network Islam et al. (2017) able to
achived 76% Recall and 84% Precision. during this research Taylor et al. (2018) used
hyperparameter tuning to achieve a result.
As we see from above Section 2 there are different datasets available for Pneumonia
classification study and so many approaches still used including Transfer learning, Deep
Neural Network, Statistical Method. Based on the previous studies we can clearly say
that on Mendeley dataset appeared twice in research, still, there are lots of new aspects
that can explore with this dataset, hence for this study Mendeley dataset is used. In
further report Methodology, Evaluation and Results are explained.
3 Methodology
From the above section 2, we can clearly say that it is hard to identify pneumonia
from chest X-ray images for any layman, it takes lots of practice and proper domain
knowledge to identify disease. There are several methodologies developed to perform the
Data mining task, in which Crisp-DM, SEEMA, and KDD process are the most famous
methodologies. The main reason to use this methodology is portability, in CRISP-DM
sequence of the project is not single directional also it can be customized easily. also we
don’t know which factors from our dataset will help to classify pneumonia, hence we are
using CRISP-DM methodology (Marb´an et al.; 2009).
CRIPS-DM methodology stands for Cross Industry-standard process and data Min-
ing1
. This Methodology contains 6 steps which are Business Understanding, Data Un-
derstanding, Data Preparation, Modelling, Evaluation, and Deployment. Below Fig. 1
shows the working structure of CRISP-DM Methodlogy.
1
https://www.ibm.com/support/knowledgecenter/en/SS3RA7 15.0.0/com.ibm.spss.crispdm.help/crisp overview.htm
6
9. Figure 1: CRISP-DM Workflow
3.1 Business Understanding
Before working on any project, it is important to have a clear objective. The main
objective of this study is to classify pneumonia from chest X-ray images with maximum
accuracy. The researcher who tried to classify pneumonia from images got a competitively
strong output. Their work has gained proficiency in their work but still there is chance
for improvements. In this study, we trying to produce better results. This model will be
major advantage for those people who are still not able to afford the expansive medical
treatment and not able to visits the medical Practitioner for diseases.
3.2 Setup
Deep learning applications like natural language processing, search engines, recommender
systems totally rely on the heavy computation on datasets, to perform this tasks parallel
computing is considered as one of the best options, parallel computing traditionally totally
rely on the Graphical Processing Units (GPUs) which leads to faster process and reliable
solutions. It’s always hard to maintain this kind of heavy system locally and performing
heavy tasks on normal computers may lead to system or hardware failure, also wastage
of time and human energy also this hardware is relatively very expensive than a normal
computer. To overcome this drawback cloud-based solutions can be a option. As per
the researcher Carneiro et al. (2018) A cloud-based platform like intel, azure, amazon is
providing these services for fair pricing. Also, pre-configured CUDA based environments
are provided by all cloud-based service provider. In this study we are going to perform
classification of the images, which include processing image files, to perform this task
requirement of heavy computation power is a must. To overcome this drawback Google
Collaboratory (a.k.a. colab) is one of the reliable solutions used.
3.3 Data Understanding
To continue this study, we need to perform image classification and all data is image-
based. The most common issue faced in images-based data is an imbalance, and it will
7
10. lead to not utilizing a neural network with full potential. In the medical domain, datasets
always have data imbalance as compare to another domain. For this study, we collected
data from Mendeley2
like Researcher Ayan and ¨Unver (2019) and Stephen et al. (2019).
Below Fig.2 shows the Chest X-ray images that is normal and pnumonia affected, This
dataset is freely available for research purposes. In June 2017 Mendeley awarded by Data
Seal of Approval which indicates, this data is retrieved from trustworthy sources.
Figure 2: Normal & Pneumonia Images
The data is already divided in the group of training, testing and validation category.
Each category having two folders called Normal and pneumonia which having images.
from the below fig. 3 the training folder normal class having a total of 1323 images and
pneumonia class having 3875 images, which clearly indicates that training data is highly
imbalanced. In testing folder total 622 images are available in which 389 images are from
pneumonia class and remaining 233 are from a normal class.
Figure 3: Training and Testing Dataset Count
2
https://data.mendeley.com/datasets/rscbjbr9sj/3
8
11. 3.4 Data Pre-processing
As mentioned earlier in Subsection 3.3, data is highly imbalance which can lead research
to unwanted output, because the Machine Learning algorithm is developed to maxim-
ize the accuracy and reduce the error and in the case of data imbalance, we will not get
proper predicted output. There are several ways to remove data imbalance in which over-
sample minority class, under-sample majority class and Synthetic Minority Oversampling
Technique (SMOTE) are involved, but SMOTE is not very practical for high dimensional
data 3
and under-sampling technique may lead to data loss which may reduce the feature
collection for pneumonia images. To avoid this issue data augmentation technique has
been performed for data pre-processing. As per Farhadi and Foruzan (2019) Data Aug-
mentation technique can used to artificially increase the volume of the dataset. training
Deep Neural network model on a large number of data can help to increase the accur-
acy of the result and the data augmentation technique can create the modified version of
same data. To perform the image augmentation keras providing the library that performs
augmentation while training model, which reduces the excess space to store new images,
In below Figure 4 shows the example of Augemnted images genrated while trainning
model, although offline augmentation helps you to create a new image and which can
store on the system if required. We procced the task using ImageDataGenerator library
which provides good range techniques, for this research specially used image rescaling,
horizontal shift, image shift, and image flip techniques. the reason to use these techniques
is, this method can use on most type of image Fujita et al. (2019) and other techniques
like denoising, segmentation and marker labeling may reduce the number of features from
images.
Figure 4: Augmented Images
In the ImageDataGenerator we set the values for techniques, in which for rota-
tion range we set to 20 degrees, for width shift range and Height shift range used 0.2
which indices the percentage (between 0 to 1), and set horizontal flip true to flip image
randomly. In the next step provided the input files using flow from directory which in-
3
https://www.datacamp.com/community/tutorials/divingdeepimbalanceddata
9
12. cludes batch size target size and enabled shuffle mode to randomly shuffle images from a
directory.
3.5 Modeling
The aim of this model to classify the difference between normal chest X-ray and pneumo-
nia chest X-ray. To continue this study Transfer learning method is selected. Previous
studies like Ko et al. (2019), Hwang et al. (2016), Taylor et al. (2018) and Islam et al.
(2017) used the Convolutional Neural Network and got state of the art result. For this
study using Islam et al. (2017) and Taylor et al. (2018), as a base paper in which the
VGG19 model is used. VGG network basically introduced by Simonyan and Zisserman
(2015) for the classification process, even though the VGG19 model having heavy para-
meters and long inference time, still this network characterized by simplicity. this model
using a 3x3 Convolutional layer to increase the depth and with Rectifier Linear Unit
(ReLU) activation function and volume size are reduced by max-pooling layer and Soft-
Max classifier used, the main reason to use this network is portability and this network
rarely used on Mendeley dataset Ayan and ¨Unver (2019). Basically, CNN is a version of
a multilayer perceptron but in regularized form, in this network each layer is connected
to other layers with neuron using a fully connected layer 4
. Each CNN model consists of
the convolutional layer + pooling layer and followed by fully connected layer. In CNN,
Convolutional layer fold the one layer’s input and pass as output to the next layer, where
each pixel is treated as a relevant variable. Basically, it’s a mathematical operation that
combine the information of two sets. In our study, a convolutional filter creates a feature
map of the image. That means it stacks all the features together and produces an output
of layer. Another layer involved in CNN is the Pooling layer, the Main task of the pooling
layer is to reduce dimension. Every time it reduces the parameter numbers which helps
to reduce the training time and encounter overfitting. The most common pooling is max
pooling which takes maximum value from the pooling window. Lastly, a fully connected
layer is used to connect every neuron from one layer to another and to classify the image,
flattened matrix passes through a fully connected layer layer5
. While creating a model
for our research dropout function is also used to prevent overfitting.
A model building or selecting model is one of the crucial tasks in CNN based classific-
ation method, selecting the proper model not only enhance the result of the classification
but also reduces extra efforts. In this study, we used transfer learning to build the model.
As per Researcher Shin et al. (2016) and Ling Shao et al. (2014) In the Transfer learning
method pre-trained model weight is used to solve other problems by training them. In
this research, the Mobile net model is used as base transfer learning. MobileNet is a
small, low powered model that is specially designed for visual classification on top of that
MobileV2 is faster than the previous version MobileNetV1 with the ability to produce the
same accuracy. MobileNetV2 contains fully convolutional layer with 32 filters followed by
19 bottleneck layer that helps to improve the performance of the model. Initially VGG19
model is imported for model building purpose but for novel approach initial top 3 layers
were excluded and added 2 more dense layers. VGG stands for Visual Geometry Group
from Oxford University and 19 are the number of layers in CNN model. usually VGG16
uses the 224x224x3 dimensions for input images but we changed that with 128x128x3
4
https://medium.com/@ksusorokina/imageclassificationwithconvolutionalneuralnetworks-
496815db12a8
5
https://cvtricks.com/tensorflowtutorial/trainingconvolutionalneuralnetworkforimageclassification/
10
13. dimensions, also ImageNet weights are used for pre-training weights and lastly disabled
the training option so no new weights will be updated while model getting trained. While
adding extra Dense layer ReLU and SoftMax activation function used, the ReLU func-
tion doesn’t create back propagation errors like sigmoid and it is also nonlinear. And the
reason to use SoftMax function is that SoftMax generates output in the range of 0 to
1(less than 0.5 considered as 0 and greater than 0.5 is considered as 1). Also, to reduce
the overfitting effect dropout rate is set. with the help of this setting, we build our model
to train for pneumonia classification. The Neural network like Xception, VGG16 and
Custom Neural network is already used on the Mendeley dataset. Thus VGG19 selected
to continue this research.
4 Design
In the Designing flow of the process is explained. Below Figure 5 clearify the steps
involved in this study. In which initially dataset is downloaded from Mendeley to carry
out this research, afterwords Data augmentation technique is used for Data preprocessing
which helps to reduce data imbalance from data. The new model is developed with base
VGG19 architecture in which Extra Dense layer and ImageNet weights added also initial
3 Layers are removed. After creating the model using training data training started
and save this model for reusability. to calculate the measurement testing data used on
the previously saved model and In the term of recall and precision used to check the
measurement.
Figure 5: Process Flow
5 Implementation
In the previous section 4, we discussed the process flow of this study, but total detail
implementation will discuss in this section. as mentioned earlier in subsection 3.2, we
need high computational power with the python based working environment and to fulfill
these requirements google collaboratory is used. Google colab is a cloud-based platform
that provides CUDA based platform, which is perfect for the heavy task. Google provides
11
14. a runtime python environment that is fully configured with all the latest Artificial Intelli-
gence (AI) libraries and robust GPU for heavy computations. Google provides a Jupyter
notebook which supports both Python 2 and python 3 environment without any prior
setup. Google colab is configured with Intel(R) Xeon(R) CPU @ 2.20GHz with 13 GB of
Memory and NVIDIA Tesla (K80) 2496 CUDA cores @560Mhz, 12GB GDDR5 VRAM
with 353 GB Storage space and all this hardware configuration is provided without any
cost. To complete this research further google colab is used and imaged based dataset is
uploaded on google drive for maximum accessibility.
As mentioned earlier subsection 3.3 Dataset is already separated in training, testing
and validation form, hence no need to used other techniques like K-fold cross-validation.
But dataset having an imbalance with a 3 to 1 ratio, which can lead to unwanted results
hence in data reprocessing, the Data augmentation technique is used. To complete data
augmentation TensorFlow’s ImageDataGenerator function is used in which horizontal flip,
width shift, and height shift are used6
. Also, Flow From Directory is another function
from keras is used to set the batch size of the data and the dimension of the images is
set to 128x128, with the same function we set to shuffle data on to shuffle data for each
epoch.
While building a model transfer learning technique is used, in which the base VGG19
model used as base architecture also initial 3 layers of the VGG19 are removed and
model.trainable function set to False so the model will not add extra weight while training.
Also while creating model 3 more dense layers added and activation function ReLU and
Softmax are used respectively. Also, to reduce the overfitting effect dropout rate is set
to 0.7.
to Compile the model a model.compile function is used, while compiling model optim-
izer, loss function and metric need to define7
. In this study, Adam optimizer algorithm is
used because Adam optimizer uses less memory and for the different parameter it com-
putes different learning rates. Secondly, loss function used for parameter estimation, for
this study categorical crossentropy is used to detect the single pneumonia class and for
the metrics, Accuracy is used.
From the figure 6 model layers can be seen. Till now, the model is built for training
purposes. now, we will train the model for classification, To initiate the training keras
providing fit generator function which helps to train the training data for N number of
iteration N will be any number or how many times model should be trained.
Figure 7 helps to illustrate model training logs, Furthermore, we set epochs for 20.
For every iteration, the model creates new data and discards the previous data. Also
to checks that how a model is performing after each iterations. validation dataset is
provided to check loss and accuracy against the validation set and all training logs are
stored for evaluation purposes.
Using evaluate generator function we check how much accuracy will get on the testing
dataset. save function from keras is utilized to save the model for future use. To find
out the recall of the model on unintroduced data. initially, stored the images from the
training dataset to their every class i.e. 0 is for normal and 1 is for pneumonia, so we
get to know which image is infected with pneumonia and which not. Furthermore using
predict Function from Keras we provided all test data to model so the model can perform
classification. At the end of this we can compare the result with the Confusion Matrix.
6
https://www.tensorflow.org/tutorials
7
https://keras.io/preprocessing/image/
12
16. In the next section 6 will explain the evaluation technique for the model trained
previously.
6 Evaluation
After the model gets trained, we need to check how the model is performed while training,
in that case, Evaluation comes in a handy way. As per Hsueh et al. (2019) Maintaining
logs of the training cycle helps a lot to understand model performance. In this study,
we maintained the log of each epoch that is ran during model training. For evaluation
purpose we will use that logs as Accuracy and loss plot, these logs will help to understand
the growth of the model while training phase8
.
6.0.1 Accuracy plot:
An accuracy plot is one of the keras tools that help to identify how the model is performing
while training. The below fig. 8 shows the accuracy plot our study, in which we can see
that accuracy is for training data is started from 0.830 and it increasing till 5th epoch
after little fluctuation accuracy plot again start to increase. During the training period
accuracy plot for the training dataset is constantly fluctuating but also increasing. For
validation data accuracy plot is showing constant development but during the training the
model accuracy is decreased but again it comes to constant level. Even though accuracy is
showing different patterns for both datasets the difference between both accuracy is least
and they do not constantly increase together so we can say that data is not overfitting
while training model.
Figure 8: Loss and Accuracy Plot
6.0.2 Loss Plot:
Loss Plot tells the story about how much data is lost by the model while training period.
Above Fig. 8 show how loss was continuously decreasing from beginning till last Epoch
8
https://keras.io/visualization/
14
17. for training data. At initially data loss was in the range of 0.30 to 0.40 but after each
iteration loss is decreased till less than 0.20. and for validation data loss is fluctuating
thorough the whole training cycle. From the Above Fig. 8 image we can clearly say that
loss for training and validation suggests that training data is not overfitting.
6.0.3 Evaluate Generator
Until now Accuracy plot and loss plot help us to find that how the model is performing
while training phase on training data, but to check the accuracy on testing data keras
providing evaluate genrator function. This function helps to find out how the model will
work on testing data, throughout this step our model got almost 0.86 accuracy on testing
data. But evaluate generator predict the performance of testing data and for measure
accuracy is considered. As this study is performing on medical dataset accuracy is not
the finest measure that we can consider for measurement because accuracy is based on
overall performance, hence recall and precision are the measure we are considering to
find out how accurate is model predicting. To calculate the recall, model need to get
unintroduced data that are not known to model yet. In that case, the test dataset
used for testing purposes. Initially, all images from the test dataset are converted into
128x128x3 dimensions and labeled each image with their respective class, that is 0 for
normal and 1 for pneumonia infected class, which will help to compare the prediction
result with original labels. With help of Predict function provided by keras the model
can test on testing dataset and store that result and to compare the predicted label with
the original label.
7 Result
As mentioned earlier measuring criteria for healthcare domain is used as Recall and
precision for more accurate classification (Deng et al.; 2016). The overall performance of
the Custom VGG19 model performed on 624 images of pneumonia and normal class. It
can be seen from the figure 9 out of 469 pneumonia cases, 383 cases are able to classify
accurately, and 148 normal cases out of 155 classified as normal.
The mathematical representation of recall and Precision will give the proper meas-
urement for the model9
.
basically,
True Positive (TP), is the case where a person has pneumonia and even model
predicted Yes.
True Negative (TN), is the case where a person doesn’t have pneumonia and model
predicted No.
False Positive (FP), is the case where a person doesn’t have pneumonia and model
predicted Yes.
False Negative (FN), is the case where a person has pneumonia and model predicted
as No.
9
https://en.wikipedia.org/wiki/Confusion matrix
15
18. Figure 9: Loss and Accuracy Plot
Based on discussed Scenarios above below are the mathematical representation of
Precision, Recall, Accuracy and F1 score.
Precision =
TP
TP + FP
(1)
Recall =
TP
TP + FN
(2)
Accuracy =
TN + TP
TP + TN + FP + FN
(3)
F1score = 2 ∗
Precision ∗ Recall
Precision + Recall
(4)
where,
TP= True Positive, FP = False Positive, TN = True Negative, FN = False
Negative
From the above formulas Recall is calculated as 0.98 and precision is calculated as
0.83, 0.851 is accuracy and 0.89 F1 Score calculated. In this study we able to get High
recall as 98% but precision is measured 83%, this means whenever we get high Recall
model compromise with Precision (Sokolova et al.; 2006).
A comparison of Previous work with the same VGG19 model is explained in Table
1, indicates that Recall for both researchers is not able to exceed more than 80% but in
terms of precision Taylor et al. (2018) achieved more precision with 91% as compared
Islam et al. (2017). The newly proposed method helps to find new result with increasing
Recall percentage as compared Taylor et al. (2018) and Islam et al. (2017). New method
able to achieve 98% recall, but it compromises Precision, during this research precision is
reduced to 83% which lower than both previous studies, Thus it’s possible to identify that
16
19. Researcher Model Recall Precision
Taylor et al. (2018) VGG-19 79% 91%
Islam et al. (2017) VGG-19 76% 84%
Proposed
Method
Custom
VGG-19
98% 83%
Table 1: Comparison table between Previous studies and Proposed Studies
there is a trade-off between these two measures which need to maintain as per depending
on the industry requirement.
8 Conclusion and Future Work
In the section 2 as we saw so many researchers contributed their work in pneumonia
classification with different techniques on different datasets, in which RSNA and ChexNet
dataset is popular for pneumonia classification due to a vast quantity of data availability.
Many researchers used Transfer learning, Deep Neural Network, Convolutional Neural
Network and also Statistical method to carry out research. In this study, a novel approach
has been developed to classify the pneumonia disease. The dataset that has been used in
this study was image-based and also rarely used for research purposes. One of the major
challenges faced in this dataset is the high-class imbalance. Thus, the data augmentation
technique has been used to overcome this problem. using the help of a Pre-trained network
and VGG19 Architecture, we used the transfer learning technique with Imagenet weights.
A novel approach of adding extra dense layers and excluding the first three layers has been
undertaken in our study. Based on the results, it has been observed that our proposed
model achieved better classification performance than Islam et al. (2017) and Taylor et al.
(2018)
In the future, we would like to extend this study on the same dataset and try to get
more precision with maintaining recall percentages, so end-user get more precise results.
9 Acknowledgement
I would like to convey my thanks to my supervisor Mr. Noel Cosgrave for his supervision
and guidance, his suggestions to read more papers helps a lot to find new possibilities
throughout this study. I would also like to thanks my family and friends for all their
support and encouragement, also grateful to Mendeley for sharing a good and rich dataset.
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