Efficient Skin Cancer Diagnosis and Classification via High-Speed Deep Learning Architecture

Skin cancer is still a major worldwide health issue, which highlights the necessity for quick and accurate detection methods. This research introduces a robust skin cancer classification system leveraging the capabilities of YOLOv8, An advanced algorithm for object detection. The proposed model processes dermatoscopic images with remarkable speed and accuracy, facilitating the identification of malignant lesions. The YOLOv8 architecture enables classification of various skin cancer types, including melanoma and non-melanoma, by effectively localizing and classifying lesions within the images. A comprehensive dataset, comprising diverse skin lesions, was employed for training and validation, ensuring the model's adaptability to different clinical scenarios. The evaluation of the proposed model demonstrates superior performance compared to traditional methods, exhibiting high sensitivity and specificity.