<![CDATA[Skin cancer is the most common type of cancer globally, and early detection is crucial for effective treatment. This research reviews the use of deep learning techniques in detecting and diagnosing skin cancer. A review of current methodologies was conducted to propose new strategies for improving the accuracy and reliability of the detection and diagnosis processes. Various deep learning models, including convolutional neural networks, were evaluated using three publicly available datasets. The PSO algorithm was utilized for segmentation and feature extraction, while also exploring the impact of transfer learning, data augmentation, and model ensemble on model accuracy. The findings of this study indicate that deep learning techniques can significantly enhance the detection and diagnosis of skin cancer]]>
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