# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
###### Abstract
AI methods can also assist practitioners in the reliable classification of chronic wounds, which ultimately helps in wound management and treatment, and saves time and cost. The significance of improving healthcare techniques utilizing AI has been investigated for years. In the past few decades, artificial intelligence has demonstrated its effectiveness in medical imaging, including brain tumor detection, cancer screenings, COVID-19 detection, etc. Thus, it enhances precision medicine, improves medical screening, evaluates patient risk factors, and reduces the workload for clinicians. In the literature, researchers have introduced several machine learning (ML) and deep learning (DL) methods for wound classification, which include wound type classification, wound tissue classification [6; 7], wound severity [8; 9], and wound depth classification [10]. However, more research needs to be performed on the multi-class classification of chronic wound types. Most existing works [11; 12; 13; 14; 15] have focused on classifying the specific regular wound type, such as diabetic foot ulcer detection, venous ulcer detection, or pressure wound detection. Multiple chronic wound type classifications can allow the DL model to become generalized for different types of chronic wounds. So, a need still exists to develop an effective model and the performance enhancement of chronic wound classification. Therefore, we propose an effective and efficient end-to-end deep learning framework that can classify the multiple types of chronic wounds from wound images, including diabetic wounds, pressure ulcers, surgical wounds, and arterial and venous ulcers. More specifically, we have proposed a Swish-ELU EfficientNet-B4 (SEEN-B4) framework, which is a fusion of two EfficientNet-B4 models with different activation functions for extracting competent feature maps. The prediction module of our SEEN-B4 model comprises dense and dropout layers, where the \(L_{2}\) regularization is applied to the second-last dense layer. Lastly, the extracted salient features are propagated to the softmax layer for accurate chronic wound classification. The major contributions of our research work include:  
1. The proposal of a novel fused Swish-ELU EfficientNet-B4 model that concatenates Swish EfficientNet-B4 and ELU EfficientNet-B4 to improve the accuracy of classifying multiple chronic wounds. 2. An extension of the existing Medetec and Advancing the Zenith of Healthcare (AZH) datasets for 3-class wound classification, to reduce the class imbalance problem of these datasets. 3. An exhibition of the explainability power and the feature selection capability of the proposed framework. Extensive experiments and cross-corpora evaluation for multi-class chronic wound classification to show the effectiveness and generalization aptitude of the proposed SEEN-B4. The remainder of the article is organized as follows: Section 2 presents and discusses the literature review, and the proposed method is described in the Section 3. Details regarding the experimental setup and results, including datasets, performance evaluation, comparison with SOTA methods, explainability, cross-corpora evaluation, ablation study, and limitations, are provided in Section 4. Finally, Section 5 presents the conclusion.