Deep Learning Driven Framework for Early and Accurate Skin Disease Detection

Abstract

In dermatology, the accurate and early identification of skin diseases through delay in treatment can worsen the patient's health and may require a longer duration of therapy. This research aims to assess the performance of Convolutional Neural Networks (CNNs) to automate the classification of images obtained from dermatoscopy and to find out whether deep learning can be a diagnostic tool used in clinics. The latest CNN structures, i.e., ResNet50, InceptionV3, and VGG16, were trained and tested on a carefully selected multi-class dermatoscopic image dataset, which was supported by data augmentation techniques to solve the problem of the class imbalance and to confine the overfitting effect. Standard metrics such as accuracy, sensitivity, specificity, and AUC-ROC, together with Grad-CAM-based visual interpretability, were used to measure the performance. The findings disclose that CNNs are able to gain a higher accuracy and a better discrimination of the features than traditional machine-learning models. The optimal model, ResNet50, was capable of not only lesion classification at a high level of accuracy but also localization for clinical purposes. This evidence suggests CNNs as a dependable and extensible resource for tackling the identification of skin diseases in the field of dermatology. However, issues such as dataset variance, computational cost, and clinical integration are still existing. This research sets deep learning as a next generation instrument which can be used by dermatologists to increase their diagnostic accuracy, and in addition, it has the benefits of being accessible and of saving time in a real-world healthcare setting.