DFU_SPNet: A stacked parallel convolution layers based CNN to improve Diabetic Foot Ulcer classification

Abstract

Diabetic Foot Ulcer (DFU) is a complication of diabetes that causes lower limb amputation. In this work, a unique stacked parallel convolution layers-based network (DFU_SPNet) is proposed to perform DFU vs. normal skin classification. The main objective of this work is to design an effective CNN-based classification model, along with proper fine-tuning of optimizer settings. DFU_SPNet consists of 3 blocks of parallel convolution layers with multiple kernel sizes, for local and global feature abstractions. The proposed DFU_SPNet, trained using SGD (with momentum) optimizer with 1e−2 learning rate on the DFUNet dataset, outperformed the current state-of-the-art results with an AUC of 0.974.