AI-Based Skin Disease Classification Using Deep Learning Techniques
TL;DR
This paper presents a comprehensive deep learning-based framework for the automated detection and classification of skin diseases from dermoscopic and clinical images, built on the ResNet50 convolutional neural network architecture and evaluated using standard classification metrics.
Abstract
Skin diseases represent one of the most widespread categories of health disorders worldwide, and timely diagnosis plays a critical role in preventing complications such as skin cancer. Conventional diagnostic procedures depend largely on visual examination by dermatologists, a process that is subjective, time-consuming, and difficult to access in rural or under-resourced regions. This paper presents a comprehensive deep learning-based framework for the automated detection and classification of skin diseases from dermoscopic and clinical images. The proposed system employs a transfer-learning approach built on the ResNet50 convolutional neural network architecture, pre-trained on ImageNet and fine-tuned on benchmark dermatological datasets, namely HAM10000, the ISIC Archive, and DermNet. The methodology encompasses dataset collection, image pre-processing, data augmentation, feature extraction, model training, disease classification, and rigorous performance evaluation. ResNet50 is selected for its residual-learning capability, which mitigates the vanishing-gradient problem and enables deeper, more accurate networks suited to fine-grained medical image analysis. An extensive review of over thirty related studies spanning convolutional architectures, ensemble methods, and emerging transformer-based models is used to position the proposed framework within the current state of the art. The framework is evaluated using standard classification metrics, including accuracy, precision, recall, specificity, F1-score, Cohen’s kappa, and confusion-matrix analysis, and is benchmarked conceptually against alternative architectures such as a baseline CNN, VGG16, MobileNetV2, DenseNet, and EfficientNet. The anticipated outcome is an accurate, scalable, and accessible screening tool capable of assisting healthcare professionals in early diagnosis, thereby reducing diagnostic delay and improving healthcare accessibility, particularly in regions with limited dermatological expertise.