A Multi-Class Skin Cancer Classification Through Deep Learning

Abstract

Skin infection is one of the most frequent diseases all over the world, and persons under the age of 40–60 have a lot of skin problems. This paper shows how to use a very efficient deep-convolution neural network to analyze and predict skin lesions as accurately as possible. The dataset was acquired from the public domain and contains over 22,900 photos that include different categories of skin lesions and of which 2726 images related to squamous cell carcinoma, malignant melanoma, and basal cell carcinoma are extracted, and the remaining images are ignored. Some of the obtained photos may contain noise; filters are used to reduce the noise in the photographs. The suggested deep-convolution neural network technique encompasses six convolution layers, to reduce the size max-pooling layer applied wherever possible, and the model was used to categorize and forecast skin cancer once the data were cleaned. The model was able to distinguish skin lesions such as squamous cell carcinoma, malignant melanoma, and basal cell carcinoma generated an average accuracy of 97.156%. The paper’s major goal is to predict skin cancer in its early stages and give the best accuracy with the least amount of error possible.