A Lightweight Model to Skin Disease Recognition Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.32913/mic-ict-research-vn.v2024.n1.1265
· OA: W4400777091
Skin disease has become increasingly prevalent, emerging as one of the most widespread conditions. It significantly affects human health and even causes skin cancer and death. Therefore, there are many methods have been proposed to solve this issue recently, especially deep learning-based methods. However, these state-of-the-art methods seem to only focus on how to achieve better performance and ignore the issue of inference time. Specifically, deep learning-based methods usually build very deep with a huge of model size and computational cost. As a result, this becomes very difficult to deploy these models on devices with no GPU support. In this study, we introduce a proficient and lightweight model designed to address this issue, leveraging the Mobilenet architecture. Our experimental findings demonstrate that the suggested network delivers comparable performance to contemporary cutting-edge techniques across diverse benchmark datasets, including HAM10000, International Skin Imaging Collaboration 2017, and International Skin Imaging Collaboration 2019. Notably, our approach utilizes merely 0.2 million parameters and 0.3 GFLOPs for image classification. This attribute holds substantial importance for deploying the model on edge devices lacking GPU support.
