A comparative Analysis of InceptionV3 and Xception Deep Learning Models for Enhanced Nail disease Diagnosis

Nail diseases, often early indicators of systemic health conditions, require accurate and timely detection for effective management. Traditional diagnostic methods often take more time and need expert intervention, making automated detection systems increasingly desirable. Deep Learning (DL) approach, in particular Convolutional Neural Networks (CNNs), have shown a path in this direction. This research work focuses on comparative study of two DL models, InceptionV3 and Xception, to diagnose nail diseases. Both models, known for their performance in image recognition tasks, were evaluated based on their accuracy, efficiency and robustness in detecting various nail diseases using nail images. The results demonstrate along with competitive performance, Xception outperforms InceptionV3 in terms of accuracy and model stability, particularly in distinguishing between subtle differences in disease categories. This work comes up with the strength of DL models in improving diagnostic tools for dermatology and contributes valuable insights into selecting optimal architectures for medical image classification tasks. Future research may explore model optimization and expansion to other skin and nail-related conditions for more comprehensive automated diagnostic systems.