Skinnet-14: A Fine-Tuned Cct Model for Classifying Skin Cancer Addressing Computational Complexity and Training Time

Abstract

In recent years, the occurrence and mortality rate due to skin cancer has increased to a higher extent worldwide. It is crucial to identify such cancers early and accurately to provide proper treatment, and research has shown that deep intelligent learning-based ways to address this issue have been proved successful. The main motivation of this study is to classify skin cancer using deep learning techniques on dermoscopy dataset with optimal performance while training time taken into account. The aim is to develop such an automated framework which can perform optimally across three different dermoscopy datasets having diverse characteristics. We have proposed a model SkinNet-14 by altering compact convolutional transformer (CCT) using 32 × 32 sized input image which results in minimizing time complexity to classify skin cancer into different classes. The SkinNet- 14 architecture is developed through ablation study conducted on CCT model using HAM dataset. Prior to that, several data augmentation techniques and preprocessing methods are applied to enhance the image quality and quantity of all the datasets. Afterwards, the proposed model is evaluated with the rest two datasets. Results show that, the model which was proposed, achieved an accuracy of 97.85% on the HAM dataset, 96.0% on the ISIC dataset, and 98.14% on the PAD dataset. Moreover, the proposed model yields better performance in terms of number of parameters, accuracy and training time than six transfer learning model while training with 32 × 32 sized images.