An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

Maria Vasiliki Sanida, Theodora Sanida (), Argyrios Sideris and Minas Dasygenis
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Maria Vasiliki Sanida: Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece
Theodora Sanida: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece
Argyrios Sideris: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece
Minas Dasygenis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece

J, 2024, vol. 7, issue 1, 1-24

Abstract: Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases.

Keywords: deep learning framework; convolutional neural network; lung diseases; optimizing; chest X-ray imaging; multi-class diagnosis (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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