AI-powered precise diagnosis and automated nail disease detection using a fused CNN-CapsNet model

Nail disease classification is a crucial task in dermatology, aiding in the early diagnosis and treatment of various conditions. In this study, we leverage an open-access dataset from Kaggle containing 3,835 images and apply data augmentation techniques, expanding the dataset to 11,505 images to improve model generalization. We propose a CNN-based deep learning model and evaluate its performance on the augmented dataset. To further enhance classification accuracy, we fuse the proposed CNN model with a Capsule Network (CapsNet), leveraging its ability to capture spatial hierarchies and complex relationships between image features. Both models are trained and evaluated, followed by a visualization of classification results. The fused CNN-CapsNet model outperforms the standalone CNN model, achieving an overall accuracy of 98.5%, demonstrating precise and secure AI-powered nail disease diagnosis, ensuring model robustness. This research highlights the advantages of combining CNNs with Capsule Networks for improved medical image analysis and classification.