勾配重み付きクラス活性化マッピングと転移学習を用いた皮膚疾患分類のための説明可能なAI、デジタルヘルスにおける輪郭特定

This research evaluates the feasibility of addressing computer vision problems with limited resources, particularly in the context of medical data, where patient privacy concerns restrict data availability. The study focuses on diagnosing skin diseases using five distinct transfer learning models based on convolutional neural networks.

Two versions of the dataset were created, one imbalanced (4092 samples) and the other balanced (5182 samples), using simple data augmentation techniques. Preprocessing techniques were employed to enhance the quality and utility of the data, including image resizing, noise removal, and blur techniques. The performance of each model was assessed using fresh data after preprocessing. We have utilized VGG19, VGG16, GoogleNet, XceptionNet, and Inception for comparing data training performance, which indicates that our preprocessing refined the image quality and texture. Therefore, the accuracy increased after augmentation, and low output reflects that the data quality before augmentation was poor.

According to the research findings, the VGG-19 model achieved an accuracy of 95.00% on the imbalanced dataset. After applying augmentation on the balanced data, the best-performing model was VGG-16-Aug with an accuracy of 97.07%. These results suggest that low-resource approaches, coupled with preprocessing techniques, can effectively identify skin diseases, particularly when utilizing the VGG-16-Aug model with a balanced dataset.

The study addresses a range of skin disorders, including acne, vitiligo, hyperpigmentation, nail psoriasis, and SJS-TEN, focusing on aspects that remain underexplored in previous research. The findings highlight the potential of simple data augmentation techniques; moreover, explainable AI: Grad-CAM interpreted the model outcome by showing image contours visually and identifying uncommon skin conditions and overcoming the data scarcity challenge. The implications of these research findings are significant for the development of machine learning-based diagnostic systems in the medical field. Further investigation is necessary to explore the generalizability of these findings to other medical datasets.