Enhanced visual insights and diagnosis of interstitial lung diseases via Mufinet-DCGAN framework
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Abstract
Accurate identification and classification of medical images are pivotal in recent medical diagnostics. Despite considerable advancements in deep learning, current methodologies face challenges in capturing nuanced details, particularly from the perspective of interstitial lung diseases (ILDs). Moreover, there is a prominent gap in the investigation of integrating sophisticated image enhancement techniques, such as contrast-limited adaptive histogram equalization (CLAHE), and classification strategies leveraging convolutional neural networks (CNNs). This study proposes a novel methodology that synergistically combines the MufiNet-DCGAN approach to enhance image resolution and refine ILD classification. Through rigorous experimentation, our proposed method achieves commendable accuracy (98.75%), precision (98.01%), recall (98.63%), and F1 score (97.99%). These results underscore the potential of the proposed approach to advance medical diagnostics by furnishing robust tools for precise disease detection.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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