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Skin Cancer is one of the most deathful of all the cancers. It is bound tospread to different parts of the body on the off chance that it is not analyzedand treated at the beginning time. It is mostly because of the abnormal growthof skin cells, often develops when the body is exposed to sunlight. TheDetection Furthermore, the characterization of skin malignant growth in thebeginning time is a costly and challenging procedure. It is classified where itdevelops and its cell type. High Precision and recall are required for theclassification of lesions. The paper aims to use MNIST HAM-10000 datasetcontaining dermoscopy images. The objective is to propose a system that detectsskin cancer and classifies it in different classes by using the ConvolutionNeural Network. The diagnosing methodology uses Image processing and deeplearning model. The dermoscopy image of skin cancer taken, undergone varioustechniques to remove the noise and picture resolution. The image count is alsoincreased by using various image augmentation techniques. In the end, theTransfer Learning method is used to increase the classification accuracy of theimages further. Our CNN model gave a weighted average Precision of 0.88, aweighted Recall average of 0.74, and a weighted f1-score of 0.77. The transferlearning approach applied using ResNet model yielded an accuracy of 90.51%

Decision Support System for Detection and Classification of Skin Cancer using CNN