Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset

TL;DR

This paper benchmarks lightweight deep learning models for skin cancer classification using the ISIC 2017 dataset, highlighting NASNetMobile's superior performance with 82% accuracy in a mobile-friendly context.

Contribution

It introduces a benchmarking framework for lightweight deep learning models on skin cancer classification and compares their performance using transfer learning and data augmentation.

Findings

NASNetMobile achieved 82% accuracy

Model with 16 batch size performed best

Lightweight models are effective for skin cancer detection

Abstract

Skin cancer is one of the deadly types of cancer and is common in the world. Recently, there has been a huge jump in the rate of people getting skin cancer. For this reason, the number of studies on skin cancer classification with deep learning are increasing day by day. For the growth of work in this area, the International Skin Imaging Collaboration (ISIC) organization was established and they created an open dataset archive. In this study, images were taken from ISIC 2017 Challenge. The skin cancer images taken were preprocessed and data augmented. Later, these images were trained with transfer learning and fine-tuning approach and deep learning models were created in this way. 3 different mobile deep learning models and 3 different batch size values were determined for each, and a total of 9 models were created. Among these models, the NASNetMobile model with 16 batch size got the best result. The accuracy value of this model is 82.00%, the precision value is 81.77% and the F1 score value is 0.8038. Our method is to benchmark mobile deep learning models which have few parameters and compare the results of the models.