An accurate and noninvasive skin cancer screening based on imaging technique

Abstract In the last decade, the public health problem is the primary consciousness worldwide, and cancer is especially the central issue. Further, skin cancer comes in the top‐3 of the world's most common cancer. We have proposed an efficient convolutional neural network (CNN) model that identifies skin cancer problems accurately. Although dataset HAM10K is used for the classification problem, its samples for each class are highly imbalanced and therefore are accountable for lower training accuracy. The AlexNet model is customized for the HAM10K data classification to address this problem. In addition, this work has presented an activation function that overcomes the vanishing gradient problem, and it is verified using the used dataset at multiple benchmark architectures. The results show better accuracy compared to the state‐of‐the‐art activation function. Our customized CNN architecture with the proposed activation function has 98.20% accuracy for HAM10K, which is better than any other state‐of‐the‐art models currently present. Further, precision, recall, and F‐score results are also improved, which are also 98.20%.