K-means clustered quantization for efficient CNN-based skin lesion classification

Official URL: https://dx.doi.org/10.1109/RTSI64020.2025.11212223

Skin lesion classification is a key predictor for early skin cancer detection. But bringing deep learning models to resource-constrained environments, such as mobile or embedded systems, requires efficiency in both accuracy and memory utilization. We present, in this paper, a Convolutional Neural Network (CNN) with 8,550,983 parameters for skin lesion classification and a K-Means-based clustered quantization technique for compressing the model while maintaining high classification efficiency. Aside from quantizing schemes standard to the industry—8-bit int, 16-bit float, and 32-bit float—we apply K-Means quantization, where the weights of a given layer are clustered into summary centroids. This will cause lower precision and redundancy, but creates an efficient but small model representation. The K-Means clustering model still had good performance with an accuracy value of 97%, precision 97.20%, recall 97.09%, and F1-score 96.95%, and compression ratio 0.1. These results indicate that K-Means-based clustered quantization has a good solution for model size minimization without performance loss, making it a great candidate for real-time skin lesion classification on embedded or mobile systems.