  Skin Cancer is one of the most deathful of all the cancers. It is bound to
spread to different parts of the body on the off chance that it is not analyzed
and treated at the beginning time. It is mostly because of the abnormal growth
of skin cells, often develops when the body is exposed to sunlight. The
Detection Furthermore, the characterization of skin malignant growth in the
beginning time is a costly and challenging procedure. It is classified where it
develops and its cell type. High Precision and recall are required for the
classification of lesions. The paper aims to use MNIST HAM-10000 dataset
containing dermoscopy images. The objective is to propose a system that detects
skin cancer and classifies it in different classes by using the Convolution
Neural Network. The diagnosing methodology uses Image processing and deep
learning model. The dermoscopy image of skin cancer taken, undergone various
techniques to remove the noise and picture resolution. The image count is also
increased by using various image augmentation techniques. In the end, the
Transfer Learning method is used to increase the classification accuracy of the
images further. Our CNN model gave a weighted average Precision of 0.88, a
weighted Recall average of 0.74, and a weighted f1-score of 0.77. The transfer
learning approach applied using ResNet model yielded an accuracy of 90.51%
