Comprehensive Summary
This article investigates if the use of MobileNetV2 implemented with TensorFlow and Keras can effectively recognize skin disease across a diverse image dataset. The author compiled a dataset of 56,000 images spanning 30 diseases, such as acne, eczema, melanoma, psoriasis, and vitiligo which were then resized and augmented. The program was trained by freezing early layers, then selectively unfreezing deeper layers for fine-tuning. MobileNetV2 showed stable training and had accuracy of around 29% and macro precision and recall around 0.30. Conditions such as acne and vitiligo were most consistently predicted, while melanoma and carcinoma had less consistency. The author emphasizes that although accuracy of using MobileNetV2 is too low for clinical decision making, it can be a lightweight system for image analysis.
Outcomes and Implications
Skin diseases affect a large portion of the global population, and early detection is often limited by the shortage of dermatologists. The automated classification systems could improve access to dermatologic screening making these lightweight systems valuable. Although the current accuracy of the MobileNetV2 system is not high enough, the study indicates the potential for AI-based assistive technology in the future.