Revolutionizing Diagnostic Insights: Exploring Advanced Image Processing Techniques and Neural Networks in Traditional Indian Medicine

The Siddha and Ayurveda traditional Indian medicine practices utilize non-invasive diagnostic methods, such as Neikuri and Taila Bindu Pariksha, for patient diagnosis through urine analysis. While these methods have proven effective for centuries, their accuracy highly depends on the subjective experience of practitioners. To address this limitation, this study explores the use of advanced image processing techniques and deep learning, specifically Convolutional Neural Networks (CNNs), to automate and enhance diagnostic image analysis. This study utilized five pre-trained CNN models, namely DenseNet, ResNet, VGG-19, Inception, and EfficientNet, on a dataset of Neikuri images acquired from a Siddha medical institute, to standardize and improve the accuracy of patient diagnosis. The comparative evaluation revealed DenseNet as the best-performing model, achieving a classification accuracy of 93.33%, while Inception v3 followed with 90.5%. This study highlights the potential of integrating modern neural networks with traditional diagnostic practices, paving the way for more objective, efficient, and accessible healthcare solutions in traditional Indian medicine.