Detection of Pneumonia from X-Ray Images Using Deep Learning Techniques (original) (raw)
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Review on Pneumonia Detection from Chest X-Ray using Deep Learning Approach
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Machine Learning (ML) provides various techniques and tools that can help solving diagnostic and prognostic problems in a variety of medical fields. Machine learning is being used for the analysis of the importance of clinical parameters and their combinations for diagnosis, e.g. prediction of disease progression, extracting medical information for outcome analysis, therapy planning and support, and for the patient management. ML is often used for data processing, such as data regularity identification by careful handling of imperfect data, continuous data analysis used in the Intensive Care Unit, and smart alarming resulting in accurate and efficient monitoring.ML can detect patterns of certain diseases in patient electronic healthcare records, and inform physicians of any anomalies. Chest X-rays are used to diagnose various diseases. Multiple diseases can be diagnosed from pneumonia to lung nodules using Deep Learning. A pre-trained ResNet-50 model is retrained with the use of different datasets of chest x-ray images. Notwithstanding major differences in datasets, ResNet-50 based diagnostic model is considered useful for pneumonia diagnosis. The trained model has achieved a 96.76 % accuracy. RSNA dataset, containing five times as many images as the Chest X-ray Image dataset, took very little time to prepare. In addition, both models were able to learn the significant features of pneumonia with a data set size of just % preparation, due to the use of the Transfer Learning technique. Still using deeper networks, the model can be improvised. The research may be expanded to identify and diagnose pneumonia with X-ray images.
A Deep Learning Approach for Classification of Pneumonia X-ray Image
Chest diseases are terribly serious health issues within the lifetime of individuals. These include chronic impeding pulmonic disease, pneumonia, asthma, T.B, and alternative respiratory organ diseases. Microorganism related respiratory illness is that the commonest sort in adults. Respiratory illness causes inflammation within the air sacs in patients' lungs. The lungs full of fluid or pus makes it troublesome to breathe. The chest X-ray is the best check for respiratory illness diagnosing. However, reading X-ray pictures will be difficult and needs domain experience and skill. It might be nice if we will simply raise a laptop to browse the pictures and generate the results. We've incontestable the practicability of classifying the chest pathologies in chest X-rays mistreatment typical and deep learning approaches. The objective of this study is to predict whether or not the person has respiratory illness from his X-ray report of lungs. For this the Convolution Neural Networking Classification model using Keras API was trained with a dataset collected from Kaggle with optimum accuracy. After fine tuning the model for two classes, the testing accuracy attained by the classifier is 94.51% with a loss of 0.06.
Multimedia Tools and Applications, 2021
Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture 'MobileNet' is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.
Healthcare
The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to...
Turkish Journal of Engineering
In recent years, the analysis of medical images using deep learning techniques has become an area of increasing popularity. Advances in this area have been particularly evident after the discovery of deep artificial neural network models and achieving more successful performance results than other traditional models. In this study, the performance comparison of different deep learning models used to efficiently diagnose pneumonia on chest x-ray images was performed. The data set used in the study consists of a total of 5840 chest x-ray images of individuals. In order to classify these data, three different deep learning models are used: Convolutional Neural Network, Convolutional Neural Network with Data Augmentation and Transfer Learning. The images in the data set were classified into two categories as pneumonia and healthy people using these three deep learning models. The performances of these three deep learning models used in classification were compared in terms of loss and accuracy. In the comparison of three different deep learning models with two different performance values, 5216 chest x-ray images in the data set were used to train the deep learning model and the remaining 624 were used to test the model. At the end of the study, the most successful performance result was obtained by convolutional neural network model applied with data augmentation technique. According to the best results of this study, this model was able to accurately predict the class of 93.4% of the test data.
International Journal of Electrical and Computer Engineering (IJECE), 2021
Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.
Pneumonia Disease Detection Using Deep Learning Methods from Chest X-Ray Images: Review
International Journal of Advanced Trends in Computer Science and Engineering , 2021
Pneumonia is an infection-related condition under which the bronchi get damaged and clogged, decreasing oxygen diffusion and causing coughing and difficulty breathing. It can cause a range of symptoms, but it is more common in vulnerable populations. Pneumonia is the leading risk factor for mortality around the globe. Annually, pneumonia kills a significant percentage of youngsters around the globe. In 2016, an estimated one million cases of bronchitis were confirmed in kids under the age of five, with 880,000 deaths. The causes and symptoms of pneumonia are explained in this paper. There are several challenges occurs during detection of pneumonia from x-ray images. Some challenges are explained in this paper. The comparison of existing method of pneumonia detection and classification is depicted with limitations. Various deep learning based architectures are discussed in this paper. The pre-trained models based on deep learning framework such as InceptionV-4, CNN, ResNet50, VGG16, VGG19.
Deep Learning-based Diagnosis of Pneumonia using X-Ray Scans
Since the beginning of 2020, the coronavirus infection which infected the world in 2019 (also known as COVID-19) has widely spread all over the globe. As it is popularly said that early detection of any disease may lead to more cures or longer survival. We established the formulations which the several models trained which is rapid and gives precise detection of viral pneumonia using chest X-ray can be very important on a large-scale epidemic testing and prevention. In this research-paper, several models have been trained based on three distinct chest x-ray datasets. The first dataset which has transpired from the publicly repository ie., Kaggle being publicly available, consisting of 5863 images, the second Dataset which consists of around 8000 images of pneumonia and the third dataset had around 22000 images. We devise the problem of distinguishing the Chest X-Ray pictures that are consisting of Pneumonia and non-pneumonia as one-class classification detection problem. It was intended to experiment with different optimizers, loss function, adding a dense layer with dropout or having a global average layer. Then, it was vital to go for the best combination of these characteristics and train transfer learning models such as inception, Xception, VGG-19, Efficient Net, InceptionResnetv2. The essential place of our task is to use "deep" learning methods and detect the diseases in scope from the designed pictures to get consistent, and high precision level of results. Nonetheless, generative, and discriminative learning are extremely novel. The motivation behind the exploration of a machine learning solution that utilizes a flexible, high-capacity CNN architecture while being efficient and fast result. Here, the description of different model choices has been given that has been found to be essential for obtaining competitive performing results. During the pandemic period artificial Intelligence (AI) has from the very beginning, been busily operating behind the screen helping the limits of human information on this huge endeavor. As we all know, machine learning is the main driving force behind AI. Then a sincere effort was invested in developing a supporting application which could help the models be utilized in a way which simulates the process of diagnosing diseases using image classification.
8TH ENGINEERING AND 2ND INTERNATIONAL CONFERENCE FOR COLLEGE OF ENGINEERING – UNIVERSITY OF BAGHDAD: COEC8-2021 Proceedings
Deep learning technique have been effectively used in resolving computer vision issues including medical image analysis. Since chest X-rays are the most frequently ordered and less expensive diagnostic imaging test, they are used as the first imaging technique to diagnose COVID-19 disease. In medical image analysis and classification, Convolutional Neural Networks (CNNs) and transfer learning are a highly effective mechanism for efficiently sharing knowledge from generic to domain-specific object recognition tasks. This work deals with the deep learning modelling as a precise tool for diagnosing and classification of five Lung Diseases (covid-19, healthy, viral pneumonia, bacterial pneumonia, lung opacity, and Tuberculosis) quickly and accurately. In this study, x-ray image dataset of covid-19 and healthy cases was collected from various locations in Iraq. The other x-ray images of other diseases were obtained from multiple publicly available xray datasets, totalling 150 images for each class. Utilizing deep and transfer learning techniques such as ResNet18, ResNet50, MobileNetv2, GoogleNet, and DenseNet201. The application and evaluating of these models are done using five-fold cross-validation the AUC (Area Under the Receiver Operating Characteristic Curve) and confusion matrices. Comparison results of these five proposed models showed that the pre-trained DenseNet201 model outperforms the other models and achieve an accuracy rate of 92%.
Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images
Advances in Science, Technology and Engineering Systems Journal
Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image's classification tasks. Through this article, we propose an implementation a CNNbased classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.