PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates. (March 2023)

• Record Type: Journal Article
• Authors: Bhosale, Yogesh H.; Patnaik, K. Sridhar
• Is Part Of: Biomedical signal processing and control. Volume 81(2023)
• Publication Date: 2023-03
• Subjects: Biomedical engineering -- Convolution neural networks (CNN) -- Ensemble deep learning -- Chronic Obstructive Pulmonary Diseases (COPD) -- COVID-19 -- Diagnosis & Classification -- Transfer learning -- Medical Imaging; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104445
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Proposed an SSE strategy with the awareness of varied class-level accuracies for different DL models. SSE models achieve superior performance by minimizing the variance of prediction errors to the competing base learners. Applied nine-class classification using chronic lung diseases with COVID-19 cases detection. The performance of the eight best influential transfer learning CNNs and 13 SSE models has been thoroughly evaluated with the proposed PulDi-COVID experimentally, showing the promising results of PulDi-COVID. The main aim of SSE is to reduce the error rate and enhance accuracy. To provide a pulmonary disease predictions study with COVID-19 disease using DL application models with X-ray images rather than laboratory findings. Several hyper-parameters, including batch size, early stopping, epochs, and optimization strategies, have been investigated. Publicly available three repositories are used with online augmentation while assessing the X-ray instances for all nine lung classes, including COVID-19. Finally, based on comparative performance analysis, perfect architecture to be produced, which will help investigators build a better practical CNN-based approach for earlier-stage identification of pulmonary illnesses with COVID-19 contamination. As evident from the explanations from recently developed systems, it's almost necessary to predict the other chronic pulmonary diseases and COVID-19 infections to avoid the mortalities of a patient. This research will be helpful for clinicians and radiologists to minimize the workload, severity, and deaths of COVID-19 patients because the mortality rate may increase as chronic lung diseases present in COVID-19 affected individuals. Abstract: Background and Objective: In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Methods: Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. Results: PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. Conclusion: The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.