Deep Convolutional Neural Network With Fusion Strategy For Skin Cancer Recognition: Model Development And Validation

This article presents a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet was trained using a dataset of 1215 clinical images of skin tumors and achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%. The performance of SkinFLNet was compared with that of three board-certified dermatologists and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. This system can potentially improve skin cancer screening accuracy in clinical practice.