Malik Fiza Saeed, Yousaf Muhammad Haroon, Sial Hassan Ahmed, Viriri Serestina
Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan.
School of Computing, College of Science, Engineering and Technology, University of South Africa (UNISA), Pretoria, South Africa.
Front Big Data. 2024 Mar 25;7:1366312. doi: 10.3389/fdata.2024.1366312. eCollection 2024.
Melanoma is one of the deadliest skin cancers that originate from melanocytes due to sun exposure, causing mutations. Early detection boosts the cure rate to 90%, but misclassification drops survival to 15-20%. Clinical variations challenge dermatologists in distinguishing benign nevi and melanomas. Current diagnostic methods, including visual analysis and dermoscopy, have limitations, emphasizing the need for Artificial Intelligence understanding in dermatology.
In this paper, we aim to explore dermoscopic structures for the classification of melanoma lesions. The training of AI models faces a challenge known as brittleness, where small changes in input images impact the classification. A study explored AI vulnerability in discerning melanoma from benign lesions using features of size, color, and shape. Tests with artificial and natural variations revealed a notable decline in accuracy, emphasizing the necessity for additional information, such as dermoscopic structures.
The study utilizes datasets with clinically marked dermoscopic images examined by expert clinicians. Transformers and CNN-based models are employed to classify these images based on dermoscopic structures. Classification results are validated using feature visualization. To assess model susceptibility to image variations, classifiers are evaluated on test sets with original, duplicated, and digitally modified images. Additionally, testing is done on ISIC 2016 images. The study focuses on three dermoscopic structures crucial for melanoma detection: Blue-white veil, dots/globules, and streaks.
In evaluating model performance, adding convolutions to Vision Transformers proves highly effective for achieving up to 98% accuracy. CNN architectures like VGG-16 and DenseNet-121 reach 50-60% accuracy, performing best with features other than dermoscopic structures. Vision Transformers without convolutions exhibit reduced accuracy on diverse test sets, revealing their brittleness. OpenAI Clip, a pre-trained model, consistently performs well across various test sets. To address brittleness, a mitigation method involving extensive data augmentation during training and 23 transformed duplicates during test time, sustains accuracy.
This paper proposes a melanoma classification scheme utilizing three dermoscopic structures across Ph2 and Derm7pt datasets. The study addresses AI susceptibility to image variations. Despite a small dataset, future work suggests collecting more annotated datasets and automatic computation of dermoscopic structural features.
黑色素瘤是最致命的皮肤癌之一，因阳光照射导致黑素细胞发生突变而引发。早期检测可将治愈率提高到90%，但误诊会使生存率降至15% - 20%。临床差异给皮肤科医生区分良性痣和黑色素瘤带来了挑战。当前的诊断方法，包括视觉分析和皮肤镜检查，都存在局限性，这凸显了皮肤科领域对人工智能理解的需求。
在本文中，我们旨在探索用于黑色素瘤病变分类的皮肤镜结构。人工智能模型的训练面临一种称为脆性的挑战，即输入图像的微小变化会影响分类。一项研究利用大小、颜色和形状等特征，探讨了人工智能在区分黑色素瘤和良性病变时的脆弱性。对人工和自然变化的测试显示准确率显著下降，这强调了诸如皮肤镜结构等额外信息的必要性。
该研究使用了由临床专家检查过的带有临床标记的皮肤镜图像数据集。基于Transformer和卷积神经网络（CNN）的模型被用于根据皮肤镜结构对这些图像进行分类。分类结果通过特征可视化进行验证。为了评估模型对图像变化的敏感性，在包含原始、复制和数字修改图像的测试集上对分类器进行评估。此外，还对国际皮肤影像协作组（ISIC）2016图像进行了测试。该研究聚焦于对黑色素瘤检测至关重要的三种皮肤镜结构：蓝白幕、点状/小球状和条纹状。
在评估模型性能时，事实证明给视觉Transformer添加卷积对于实现高达98%的准确率非常有效。像VGG - 16和DenseNet - 121这样的CNN架构达到了50% - 60%的准确率，在使用除皮肤镜结构之外的特征时表现最佳。没有添加卷积的视觉Transformer在不同测试集上的准确率降低了，这显示出它们的脆性。预训练模型OpenAI Clip在各种测试集上始终表现良好。为了解决脆性问题，一种缓解方法是在训练期间进行大量数据增强，并在测试时使用23个变换后的副本，从而保持准确率。
本文提出了一种利用Ph2和Derm7pt数据集中的三种皮肤镜结构的黑色素瘤分类方案。该研究解决了人工智能对图像变化的敏感性问题。尽管数据集较小，但未来的工作建议收集更多带注释的数据集，并自动计算皮肤镜结构特征。
