An Explainable Deep Transfer Learning Approach with Augmentation for Chest X-Ray-Driven Pulmonary Disease Diagnosis
DOI:
https://doi.org/10.30953/thmt.v10.594Keywords:
Chest X-ray, clinical decision support, ConvNeXt, deep transfer learning, EfficientNetV2, ensemble learning, pulmonary disease detection, Swin TransformerAbstract
Chest X-ray imaging plays a vital role in the evaluation of thoracic organs, serving as a primary diagnostic tool for various pulmonary conditions. Interpretations of chest X-ray reports can sometimes differ from a physician’s clinical judgment, leading to diagnostic inconsistencies and potential delays in treatment. This study explores whether the application of advanced deep transfer learning techniques can improve the accuracy of chest X-ray interpretation. Deep Learning presents powerful capabilities for processing and interpreting complex imaging data. Convolutional Neural Networks (CNNs) are widely used in deep learning to perform image classification through hierarchical feature extraction. training deep models with large annotated datasets often demands significant computational resources. In this research, MobileNet and Inception V3 architectures are employed to detect diseases such as lung cancer, pneumonia, and tuberculosis from chest X-ray images. Conducted a detailed evaluation of these models and compare their performance with traditional diagnostic approaches. The results demonstrate notable improvements in accuracy and sensitivity, confirming that deep transfer learning techniques significantly enhance diagnostic outcomes. This suggests that MobileNet and Inception V3 can serve as reliable, efficient tools to support radiologists in early detection and decision-making, and contributing to more timely and accurate medical diagnoses.
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Copyright (c) 2025 R. Sriramkumar, M.Tech, K. Selvakumar, PhD, J. Jegan, PhD2
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