AESPNet: Attention Enhanced Stacked Parallel Network to improve automatic Diabetic Foot Ulcer identification

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, and it may cause lower limb amputation. However, the manual diagnosis of DFU is a complicated and expensive process. The primary objective of this work is to design an efficient Convolutional Neural Network (CNN) approach to identify DFU. Therefore, a novel CNN-based approach (AESPNet) is proposed in this paper, where convolution layers are stacked together in a parallel fashion and with an intermediate attention module to perform DFU vs -normal skin classification. The AESPNet consists of 2 blocks, where varying-sized kernel convolution layers are connected in a parallel fashion for better local and global feature abstraction. A bottleneck attention module is associated after every concatenation operation in the network. The Stochastic Gradient Descent (SGD) (with momentum) optimizer with 1e -2 learning rate is used to train the proposed network on a privately accessed DFU dataset. The results of the proposed approach are compared with other standard CNN-based schemes, namely AlexNet, VGG16, DenseNet121, and InceptionV3. It has been found that the proposed AESPNet outperforms state-of-the-art schemes with a sensitivity score of 98.44% and 0.98 F1-Scores.

Automated summary

This paper presents an efficient approach based on Convolutional Neural Network (CNN) called AESPNet for the identification of Diabetic Foot Ulcer (DFU). Compared with other standard CNN-based schemes, AESPNet demonstrates better performance in DFU classification.