Enhancing Skin Cancer Detection Through Automated Classification Using Convolutional Neural Networks

Skin cancer presents a significant public health concern, necessitating early detection and intervention to mitigate life-threatening consequences. Despite its prevalence, skin cancer is not always given the attention it deserves. In this paper, we propose an automated procedure for distinguishing skin cancers using several neural network algorithms. We used the MobileNet architecture whose accuracy is not very high and this is why we have also used other algorithms such as Sequential CNN (Convolution Neural Network), VGG16, and Xception to improve the accuracy of our model. By comparing these algorithms, we found that the Sequential CNN performed best with an accuracy of 94%. The proposed model utilizes the Sequential CNN algorithm to achieve a high level of accuracy, which is of utmost importance for the early detection of skin cancer. The high accuracy of the proposed model makes it a promising tool for detecting skin cancer early.