CCN - Based Melanoma Anamoly Detection Model

Skin issues are a global health concern as they are impacted by both heredity and the environment. Skin cancer has a high fatality rate, although it is curable with simple surgery if caught early enough. Nevertheless, the present diagnostic techniques are laborious to process and yield inaccurate findings. With an accuracy of $\mathbf{6 0 - 8 0 \%}$, high-speed cameras show promise. By precisely identifying and classifying skin cancer lesions as either malignant or non-malignant, this study seeks to expedite the analysis of these lesions for the benefit of both patients and medical experts. Higher diagnostic efficiency in deep learning is provided by convolutional neural networks (CNNs). To improve the assessment of skin cancer, we propose CNNs. CNN uses standardized datasets to diagnose various types of skin cancer. The accuracy of the model was about $\mathbf{9 8. 6. 7 \%}$. The model demonstrate how deep learning can help achieve the goal.