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

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.

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features

“…Sanida et al [10] introduced a new deep learning (DL) framework for lung disease diagnosis using chest X-ray images (21,165 chest X-ray images). They employed the modified VGG19 model for multi-class classification (fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia) and achieved an accuracy of 98.88%.…”

“…The proposed image pre-processing technique stood out from existing methods. Sanida et al [10], Abubakar et al [11], and Ragab et al [18] mainly focused on resizing images. Guail et al [9] used augmentation, and Nahiduzzaman et al [15] emphasized resizing and normalization, often insufficient.…”

“…Our DCNN architecture introduces unique features that distinguish it from existing models. We have improved our architecture compared to other CNNs [9], modified VGG19 [10], EfficientNet [12], CNN-ELM [15], DenseNet121 [16], and DenseNet201 [17]. This is because it uses new connection patterns and optimization strategies, which makes it better at many tasks and datasets.…”

“…In binary classifications [9,14,16,17,20,21], Liang et al [19] achieved an impressive highest accuracy of 98.5% for COVID-19 and normal, while Jin et al [16] recorded a comparatively lower accuracy of 80.0% for COVID-19 and non-COVID-19. For multi-class classification [5,8,[10][11][12][13]15,20], Nahiduzzaman et al [15] addressed 17 classes but did not achieve remarkable accuracy, reaching only 90.92%. Kufel et al [12] achieved a lower accuracy of 84.28% for classifying fifteen classes of chest X-ray images.…”

“…Addressing these challenges and enhancing clinical diagnosis, we employed an automated system for chest radiographic images, which can efficiently diagnose respiratory issues. Numerous recently developed automated systems were examined in our literature review [5,[9][10][11][12][13][14][15][16][17][18][19][20][21]. Challenges were encountered by those systems in data handling during image processing and optimal feature extraction, complicated quantification, and high runtime complexity issues in classifying chest X-ray images.…”