Enhanced CNN Based Super pixel Classification for Automated Wound Area Segmentation


Biswas, Topu and Ahmad Fauzi, Mohammad Faizal and Abas, Fazly Salleh and Nair, Harikrishna K.R. (2020) Enhanced CNN Based Super pixel Classification for Automated Wound Area Segmentation. In: IEEE Region 10 Humanitarian Technology Conference 2020 (R10HTC2020), 01-12-2020, Virtual, Kuching, Malaysia.

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With the increasing prevalence rate of diabetes and obesity worldwide, chronic wounds are becoming a significant burden for world health and economy. The treatment of a chronic wound goes through complex and time-intensive process. During the healing period, continuous wound measurement helps clinicians to predict the healing time and monitor the treatment efficiency. Current clinical techniques such as ruler-based or tracing-based methods are inaccurate, time-consuming and also subject to intra- and inter-reader variability that does not satisfy a comprehensive clinical benchmark. In this paper, we proposed a method for wound boundary demarcation and estimation based on superpixel segmentation and classification using an enhanced convolution neural network. An overall accuracy, sensitivity, and specificity of around 90% was observed, which fared much better against traditional methods.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Wound segmentation, superpixel classification, convolutional neural network, wound measurement
Subjects: Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 26 Oct 2021 04:39
Last Modified: 26 Oct 2021 04:39
URII: http://shdl.mmu.edu.my/id/eprint/8542


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