Melanoma grows fast and new cases increase each year, above all in people who have light skin that develops freckles after a short stay in strong sun. Detect the tumour at the first stage plus the outcome changes completely, in the same way that a spark caught at the instant it crackles prevents a fire. Dermoscopy allows physicians to inspect clear skin structures without a cut but reading those enlarged patterns, which look like faint honeycomb lines on the surface, demands calm and long training. In this paper, I describe a deep learning system that labels melanoma in dermoscopic pictures but also finds tiny brown and red dots that a quick look would miss. The system first cleans every picture with advanced filters, locates each lesion with exact segmentation as well as runs a hybrid feature extractor built on CNNs that pulls sharp detail from every pixel, similar to light that passes through a lens and converges. We aim for high sensitivity or high specificity, and we check that the system stays accurate on all skin colours next to lesion forms, even on rough, irregular zones where shadows once deceived earlier hand coded programs. The process starts when the system sends each picture through preparation - it sharpens borders and corrects light, as though it wipes dust from a camera lens before the next shot. This action raises contrast; evens colour plus removes small defects like lone hairs or a bright glare on the skin. After that, the lesion segmentation module starts - it runs a deep encoder - decoder network that isolates the exact lesion border.
Melanoma detection, Dermoscopic image analysis, Skin cancer classification, Deep learning, Convolutional Neural Networks (CNN), Medical image processing, Computer-aided diagnosis, Image segmentation, Artificial intelligence in healthcare
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