Citation

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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Abstract

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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