Classification of Skin Diseases with Deep Learning Based Approaches
TL;DR
The proposed system uses Convolutional Neural Networks (CNNs) and related deep learning techniques to automatically learn discriminative visual features from skin-lesion images and classify disease categories to demonstrate the potential of artificial intelligence in dermatology.
Abstract
Skin diseases represent a significant global health concern, requiring accurate and timely diagnosis for effective treatment. This paper presents a deep learning-based approach for classification of skin diseases using medical image analysis. The proposed system uses Convolutional Neural Networks (CNNs) and related deep learning techniques to automatically learn discriminative visual features from skin-lesion images and classify disease categories. Image preprocessing and data augmentation are used to improve consistency and robustness. The workflow covers image acquisition, preprocessing, augmentation, model training, validation, testing, and performance analysis. The system is intended to assist healthcare professionals by providing rapid image-based decision support while reducing dependence on purely manual screening. Standard measures such as accuracy, precision, recall, F1-score, and confusion-matrix analysis are considered for evaluation. The approach demonstrates the potential of artificial intelligence in dermatology while recognizing that dataset quality, external validation, interpretability, and clinical supervision remain essential for responsible deployment.