Enhancing Interstitial Lung Disease Diagnosis Through Deep Learning Frameworks

The study involves an in-depth analysis of an Interstitial Lung Disease (ILD) diagnosis using deep learning frameworks with substantial datasets encompassing diverse medical images. Specifically, Convolutional Neural Network (CNN) architectures, including Inception V3, DenseNet-121, and VGG-16, are implemented to facilitate early and accurate identification of a range of lung diseases. The dataset employed in this research comprises an extensive collection of 5866 high-resolution computed tomography (HRCT) scans, enhancing the robustness and generalizability of the models. This study contributes to the ongoing efforts to improve ILD diagnosis through rigorous experimentation and evaluation with 95% precision on Inception V3, DenseNet-121, and 97% on VGG-16, ultimately offering potential benefits for clinical practice and patient care.