article · Journal of Intelligent & Fuzzy Systems
Manual examination of medical imaging for disease detection is time-consuming and prone to human error. An automated classification architecture, designated the Worried Deep Neural Network (WDNN), employs transfer learning to identify COVID-19 from computed tomography scan images. To address data shortages, the method incorporates data augmentation to expand positive class samples alongside image normalisation for uniform sizing. The architecture was evaluated on a dataset of 2,623 images divided into training, validation, and testing sets, and compared against pre-trained models including InceptionV3, ResNet50, and VGG19. The WDNN model reached 99.046 per cent accuracy, 98.684 per cent precision, 99.119 per cent recall, and an F-score of 98.90 per cent. These outcomes surpassed both traditional machine learning techniques and standard convolutional neural networks, presenting an automated option to assist diagnostic workflows.
Rapid and accurate diagnosis is critical during widespread infectious disease outbreaks. Manual review of CT scans requires substantial clinical time and carries risks of human error. Deploying high-performing automated classification systems can assist doctors by reducing diagnostic turnaround times, facilitating prompt patient management, and providing consistent decision support when healthcare facilities experience severe pressures on clinical personnel and resources.
The work addresses automated clinical diagnostic support, targeting medical practitioners who interpret CT scans. Evaluated experimentally on a dataset of 2,623 images, the model represents applied research that has been tested in a computational setting. Real-world commercialisation would require integration into hospital picture archiving systems, clinical validation studies, and medical device regulatory clearance before it can function as an alternative diagnostic tool.
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Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID-19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training, 524 validation, 524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 in terms of accuracy, precision, recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.
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DOI: 10.3233/jifs-201985
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