Transfer learning with class-weighted and focal loss function for automatic skin cancer classification

TL;DR

This paper presents a deep learning system using transfer learning, class-weighted and focal loss functions, and ensemble ResNet50 models to classify skin lesions into seven categories with high accuracy, aiding dermatologists.

Contribution

It introduces a novel ensemble of modified ResNet50 models with class-weighted and focal loss for improved skin cancer classification accuracy.

Findings

Top-1 accuracy of 93% on HAM10000 dataset

Top-3 accuracy of 99% on skin lesion classification

Effective use of transfer learning and loss functions for imbalanced data

Abstract

Skin cancer is by far in top-3 of the world's most common cancer. Among different skin cancer types, melanoma is particularly dangerous because of its ability to metastasize. Early detection is the key to success in skin cancer treatment. However, skin cancer diagnosis is still a challenge, even for experienced dermatologists, due to strong resemblances between benign and malignant lesions. To aid dermatologists in skin cancer diagnosis, we developed a deep learning system that can effectively and automatically classify skin lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6) Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train the system. An end-to-end deep learning process, transfer learning technique, utilizing multiple pre-trained models, combining with class-weighted and focal loss were applied for the classification process. The result was that our ensemble of modified ResNet50 models can classify skin lesions into one of the seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%, respectively. This deep learning system can potentially be integrated into computer-aided diagnosis systems that support dermatologists in skin cancer diagnosis.