Paper Title
SKIN DISEASE CLASSIFICATION USING DEEP LEARNING ALGORITHMS

Abstract
Abstract - Skin diseases pose a significant global health challenge, and effective treatment requires accurate and timely diagnosis. The traditional diagnostic methods often rely on visual observation by dermatologists, resulting in subjective interpretations and time-consuming procedures. In recent years, deep learning algorithms have emerged as powerful tools for automated medical image analysis, which can provide more accurate and responsive analysis also has increased. This research examines the use of state-of-the-art deep learning algorithms, specifically YOLOv8, Deep CNN, and ResNet50, to classify skin diseases using dermatological images. The classification of skin conditions significantly depends on the ability to extract essential features. A comprehensive and diverse dataset encompassing various skin conditions was utilized for training and validation to ensure that the models could be effectively generalized across different diseases. Transfer learning techniques are incorporated into each algorithm, utilizing pre-trained models on large image datasets to enhance adaptability and generalization to novel data. As the field of medical image analysis continues to advance, the utilization of the deep learning algorithms in skin disease classification exemplifies a multifaceted approach towards achieving efficient and accurate diagnosis, ultimately benefiting both healthcare practitioners and patients. The deep Learning models proposed in this paper offer superior performance in complex skin disease classification tasks and outperform Machine Learning models. Keywords - Deep Learning, Yolov8, Deep-CNN, DCNN, Resnet50, Medical Imaging, Skin Disease Classification.