SCD-NAS: Towards Zero-Cost Training in Melanoma Diagnosis

Hongyan Xu, Xiu Su, Arcot Sowmya, Ian Katz, Dadong Wang

Abstract: Diagnosing melanoma remains challenging despite advances in Convolutional Neural Networks (CNNs) for skin cancer detection. Their application in clinical settings is often limited by differences between natural and clinical images. To address this, we introduce the Skin Cancer Detection Neural Architecture Search (SCD-NAS) framework. In our method, Large Language Model (LLM) is leveraged as a proxy, which helps SCD-NAS achieve cost-free training. Additionally, to maximize the benefits of various architectural design spaces, we introduce a Search Space Expansion (SSE) methodology. This effectively combines the merits of diverse architectural configurations, thereby enhancing model performance. We conducted experiments on the ISIC 2020, MedMNISTv2, CIFAR-10 and CIFAR-100 datasets. Our SCD-NAS-derived ResNet50 model achieved an Area Under the Curve (AUC) of 91.23% on the ISIC 2020 dataset, improving the baseline by 5.93%. It also exceeded the CIFAR-10 benchmark by 2.45% in accuracy.