Published Paper Details:

This paper describes an intelligent, automated system for the early diagnosis of nail diseases using deep learning techniques. Nail diseases may represent other health conditions, such as fungal infections, skin diseases, and systemic diseases. Existing methods for diagnosing nail diseases still rely on visual, manual inspections or thin nail clippings, which are relative, subjective assessments, manual, and difficult to scale. The proposed system utilizes deep learning, specifically convolutional neural networks, and custom implementation of the well-known VGG16 and GoogLeNet architectures, to classify nail images into eight different diseases. Feature-level and decision level fusion provided a vehicle to improve prediction accuracy by taking advantage of the strengths of each architecture. We trained the mode on a custom dataset of annotated nail images, performing appropriate preprocessing and augmentation, and produced a user-friendly, real-time disease prediction system, using Streamlit, through the provision of a frontend user interface, simply requiring users to upload their nail images . Evaluation metrics such as overall accuracy, precision, recall, and f1-score indicate that the proposed fusion-based architecture performs better than either individual model. Overall, the system provides a good alternative to existing invasive methods for diagnosing nail diseases, especially for healthcare access with certain health challenges such as being in remote locations. The system represents an alternative early diagnosis model, which is automated, non-invasive, and scalable.