| 摘要: | 隨著醫療科技的快速發展，人工智慧（AI）與深度學習技術逐漸被應用於輔助診斷領域。皮膚疾病是臨床上最常見的疾病之一，然而在偏遠地區，皮膚科醫師資源分配不均，造成患者無法及時獲得正確診斷。同時，部分皮膚疾病之間外觀相似，非專業醫師或一般患者難以分辨，導致延誤治療。此外，皮膚癌（特別是黑色素瘤）具有高度惡性與致死率，早期辨識對於提升存活率至關重要。
本研究以深度學習為核心，旨在建立一套「皮膚疾病與癌症的辨別模型」，期望透過影像自動化分析協助臨床判別，減輕醫療人員負擔並提升診斷效率。研究中使用公開皮膚疾病資料庫共10064張影像，涵蓋五類主要皮膚疾病（含良性與惡性腫瘤）及正常皮膚影像。模型採用EfficientNetV2深度學習架構，並結合線上資料擴增（online data augmentation）策略，於模型訓練過程中即時生成多樣化影像樣本，以提升模型對於不同拍攝條件與影像變異的適應能力。
在資料擴增設計上，本研究除採用幾何變換、色彩變換與雜訊擾動外，亦引入 Cutout遮擋擴增方法，透過隨機遮擋局部影像區域，模擬臨床影像中常見的遮蔽與影像缺陷情境，以增強模型對關鍵病灶特徵的學習能力並提升其泛化表現。實驗結果顯示，所建立之模型於測試資料集上可達約83%的分類準確率，顯示其具備良好的辨識能力與潛在臨床應用價值。
本研究成果可作為皮膚疾病早期偵測與輔助診斷之基礎，未來若與臨床影像系統整合，有望協助醫療資源不足地區進行初步篩檢，提升診療效率與病患治療成效。
;With the rapid advancement of medical technology, artificial intelligence (AI) and deep learning techniques have gradually been applied to the field of computer-aided diagnosis. Skin diseases are among the most common clinical conditions; however, in remote areas, the unequal distribution of dermatology specialists prevents patients from obtaining timely and accurate diagnoses. In addition, some skin diseases exhibit similar visual appearances, making them difficult to distinguish for non-specialists or patients, which may lead to delayed treatment. Furthermore, skin cancers—particularly melanoma—are highly malignant and associated with high mortality rates, and early identification is crucial for improving patient survival.
This study is centered on deep learning and aims to establish a skin disease and cancer classification model. Through automated image analysis, the proposed approach is expected to assist clinical decision-making, reduce the workload of medical professionals, and improve diagnostic efficiency. A publicly available skin disease dataset containing 10,064 images was used in this study, covering five major categories of skin diseases (including both benign and malignant tumors) as well as normal skin images. The model is based on the EfficientNetV2 deep learning architecture and incorporates an online data augmentation strategy, in which diverse image samples are generated dynamically during the training process to enhance the model’s adaptability to different imaging conditions and variations.
In terms of data augmentation design, in addition to geometric transformations, color variations, and noise perturbations, Cutout augmentation was introduced. By randomly masking local regions of the input images, this method simulates common occlusions and image defects encountered in clinical photography, thereby strengthening the model’s ability to learn discriminative lesion features and improving its generalization performance. Experimental results show that the proposed model achieves approximately 83% classification accuracy on the test dataset, indicating strong recognition capability and promising potential for clinical application.
The results of this study provide a foundation for early detection and computer-aided diagnosis of skin diseases. With future integration into clinical imaging systems, the proposed model may assist preliminary screening in regions with limited medical resources, ultimately enhancing diagnostic efficiency and patient treatment outcomes. |
