Skin Cancer Classification: A Transfer Learning Approach Using Inception-v3

Abstract

In the human body, the skin serves as the primary layer of defense for essential organs. However, as a result of ozone layer degradation, exposure to UV radiation, fungal and viral infections.  Skin cancer is becoming more common.

This study proposes a novel deep learning-based framework for the multi-classification of eight different types of skin cancer. The suggested framework is divided into several steps. The initial phase is the data augmentation of images. In the second step, deep models are fine-tuned. The model is opted, for Inception-v3, and updated their layers. In the third step, The suggested model has been applied to train both fine-tuned on augmented datasets.

After optimization, the pre-trained model performs well for classifying skin tumors, with Inception-v3 having accuracy and an F-score of 81% and 81%, respectively.