Classification of Melanoma Skin Cancer using Transfer Learning

Abstract

Abstract: Melanoma, a fatal variety of skin cancer, is a significant public health issue on a global scale. For patients to have better results, melanoma must be detected accurately and early. In this study, transfer learning techniques are used in deep learning to provide a novel method of melanoma cancer detection. By using pre-trained convolutional neural networks (CNNs), such as VGG, MobileNet, and EfficientNet, as feature extractors, the research takes advantage of the capability of transfer learning. These networks, which were first created for tasks involving generic image identification, are refined using a large dataset of dermoscopic images of skin lesions in order to adapt them to the particular needs of melanoma detection. Essential preprocessing techniques, including image resizing, image normalization, and lesion segmentation using the Otsu segmentation method, are used to improve the dataset's quality and consistency. Image normalization has been used to reduce the complexity and processing time of the image. Skin lesions have been segmented using Otsu segmentation. These procedures increase the robustness of the melanoma detection model and help to prepare the images for efficient training. The findings of this study highlight the possibility for an earlier and more precise diagnosis and provide encouraging insights into the viability of transfer learning as a formidable tool for melanoma detection. The overall objective of this work is to improve the diagnosis and patient care for melanoma cancer. This work contributes to ongoing efforts to provide practical and effective tools for dermatologists and healthcare professionals.