Image Classification of Skin Cancer Using Deep Neural Networks with Scaling Laws
DOI:
https://doi.org/10.62051/ijcsit.v3n2.12Keywords:
Deep neural network, Image classification, HAM10000, Skin cancer, Neural scaling lawsAbstract
Skin cancer image classification is critical to improve healthcare outcomes. Current practice often involves time-consuming procedures that may delay diagnosis until the disease has progressed to an advanced stage, reducing the chances of successful treatment. This challenge is further exacerbated by the worldwide shortage of skilled dermatologists. In this study, we investigate the effect of dataset size on the image classification performance of eight networks (AlexNet, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, ViT, and MLP-Mixer). We trained these classifiers using different ratios (e.g. 1% to 100%) of samples from the HAM10000 dataset. Our experiment reveals the complex interplay among dataset size, model complexity, and skin cancer classification performance, validating the rules of the neural scaling laws on skin cancer image classification. This work highlights the impact of dataset scale and model complexity on improving the skin cancer image classification performance to potentially reduce the burden on healthcare professionals.
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