Multi‐Wound Classification: Exploring Image Enhancement and Deep Learning Techniques - Prince Odame; Maxwell Mawube Ahiamadzor; Nana Kwaku Baah Derkyi; Kofi Agyekum Boateng; Kelvin Sarfo‐Acheampong; Eric Tutu Tchao; Andrew Selasi Agbemenu; Henry Nunoo‐Mensah; Dorothy Araba Yakoba Agyapong; Jerry John Kponyo (Engineering Reports)
ABSTRACT Wounds contribute to 30%–42% of hospital visits and 9% of deaths but remain underreported in Africa. Diseases and surgeries increase wound prevalence, especially in rural areas where 27%–82% of people live, and health facilities are poor or non‐existent. This research aims to design a disease‐related wound classification model for online diagnosis and telemedicine support for traditional health practitioners and village health workers. This paper focuses on wounds from diabetic ulcers, pressure ulcers, surgery, and venous ulcers. The approaches used included Contrast Limited Adaptive Histogram Equalization (CLAHE) with machine and deep learning models, Discrete Wavelet Transformations (DWT) with a novel Gated Wavelet Convolutional Neural Network (CNN) model, and FixCaps, an improved version of Capsule Networks utilizing Convolutional Block Attention Module (CBAM) to reduce spatial information loss. The performance metrics showed similar results for the first two approaches, but FixCaps was the most proficient, with accuracy, precision, recall, and F‐score of 93.83%, 95.41%, 88.63%, and 90.93% respectively. FixCaps had trainable parameters of about 8.28 MB compared with the 195.64 MB of the Gated Wavelet CNN Model.