Comparing the capabilities of transfer learning models to detect skin lesion in humans

Abstract

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images. © IMechE 2020.