# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
###### Abstract  
Chronic wounds affect the lives of millions of individuals globally, and due to substantial medical costs, treating chronic injuries is very challenging for the healthcare system. The classification of regular wound type is essential in wound care management and diagnosis since it can assist clinicians in deciding on the appropriate treatment method. Hence, an effective wound diagnostic tool would enable clinicians to classify the different types of chronic wounds in less time. The majority of the existing chronic wound classification methods are mainly focused on the binary classification of the wound types. A few approaches exist that classify chronic wounds into multiple classes, but these achieved lower performances for pressure and diabetic wound classification. Furthermore, cross-corpus evaluation is absent in chronic wound type classification, in order to better evaluate the efficacy of existing methods on real-time wound images. To address the limitations of the current studies, we propose a novel Swish-ELU EfficientNet-B4 (SEEN-B4) deep learning framework that can effectively identify and classify chronic wounds into multiple classes. Moreover, we also extend the existing Medetec and Advancing the Zenith of Healthcare (AZH) datasets to deal with the class imbalance problem of these datasets. Our proposed model is evaluated on publicly available AZH and Medetec datasets and their extended versions. Our experimental results indicate that the proposed SEEN-B4 model has attained an accuracy of 87.32%, 88.17%, 88%, and 89.34% on the AZH, Extended AZH, Medetec, and Extended Medetec datasets, respectively. We also show the effectiveness of our method against the existing state-of-the-art (SOTA) methods. Furthermore, we evaluated the proposed model for the cross-corpora scenario to demonstrate the model generalization aptitude, and interpret the model's result through explainable AI techniques. The experimental results show the proposed model's effectiveness for classifying chronic wound types. chronic wound; EfficientNet-B4; Medetec dataset; AZH dataset; wound classification Article  
A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification  
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wounds will continue to be an economic, social, and clinical challenge as the population grows. The worldwide chronic wound care market is expected to rise from $12.36 billion in 2022 to $19.52 billion in 2029, at a CAGR of 6.7% over the forecast period, according to the market research report published in March 2022 [4]. These statistics indicate the prevalence of chronic wounds, like a \"silent epidemic\", and their financial impact on the healthcare industry. The most common types of chronic wounds include diabetic, pressure, venous, and arterial ulcers. These wound types prevent healing because of pathophysiological phenomena such as non-receptive epidermal or dermal cells to reparative stimuli, the existence of drug-resistant microbial biofilms, persistent infections, and prolonged inflammation [2]. Cardiovascular disease, diabetes, cancer, obesity, poor nutrition, and aging can also make it difficult for wounds to recover without specialized care. Sometimes, patients with injuries cannot gain access to specialized wound care and recommendations because of the shortage of wound specialists in primary healthcare sites, or due to pandemics like COVID-19 [5]. Furthermore, the correct wound class must be detected before the appropriate treatment and medication, which is manually performed by experts. Manual evaluation or the classification of a wound is time-consuming, and it requires a lot of experience and time to train a medical practitioner [5]. So, there is a need for the development of artificial intelligence (AI)-based diagnostic tools that can automatically classify chronic wounds and aid patients in remote or pandemic areas.