Multiple EffNet/ResNet Architectures for Melanoma Classification

TL;DR

This paper introduces a novel melanoma classification model combining EffNet and ResNet that leverages both image data and patient-level contextual information, achieving high accuracy and outperforming previous methods.

Contribution

The study presents a new model integrating patient context with image analysis for melanoma classification, enhancing diagnostic accuracy over existing approaches.

Findings

Achieved 0.981 accuracy in melanoma classification

Obtained 0.976 ROC value, surpassing state-of-the-art methods

Utilized patient-level information to improve diagnosis

Abstract

Melanoma is the most malignant skin tumor and usually cancerates from normal moles, which is difficult to distinguish benign from malignant in the early stage. Therefore, many machine learning methods are trying to make auxiliary prediction. However, these methods attach more attention to the image data of suspected tumor, and focus on improving the accuracy of image classification, but ignore the significance of patient-level contextual information for disease diagnosis in actual clinical diagnosis. To make more use of patient information and improve the accuracy of diagnosis, we propose a new melanoma classification model based on EffNet and Resnet. Our model not only uses images within the same patient but also consider patient-level contextual information for better cancer prediction. The experimental results demonstrated that the proposed model achieved 0.981 ACC. Furthermore, we note that the overall ROC value of the model is 0.976 which is better than the previous state-of-the-art approaches.