Inception-ResNet-V2 Based Skin Lesion Classification for Early Detection and Treatment

Skin cancer is a common and potentially life-threatening disease that requires early detection for successful treatment. Recently, various techniques involving artificial intelligence (AI) and Deep learning (DL) have been applied to skin cancer detection, offering the potential to enhance the performance and speed of diagnosis. In this paper we discuss different forms of skin cancer and their detection challenges and also presented a comprehensive review of the literature on various AI and DL techniques used for detection. We proposed a novel AI-based approach called Inception-ResNet-V2 for early skin cancer detection. The proposed approach is a variant of convolutional neural network (CNN) architecture to analyze skin lesion images and accurately identify the presence of cancerous cells. Using a publicly accessible dataset, we assessed the performance of the suggested strategy and contrasted it with currently available state-of-the-art techniques. Our findings show that the suggested strategy performs the best and is superior to existing techniques in all the areas. Overall, this research paper highlights the potential of AI and DL techniques for skin cancer detection and proposes a promising new approach that can contribute in enhancing the performance and effectiveness of skin cancer diagnosis. The suggested method may be applied in clinical settings and can assist dermatologists in making quicker and more precise diagnosis of skin cancer.