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Attention Mechanism Guided Deep Regression Model for Acne Severity Grading

Alzahrani, Saeed; Al-Bander, Baidaa; Al-Nuaimy, Waleed

Attention Mechanism Guided Deep Regression Model for Acne Severity Grading Thumbnail


Authors

Saeed Alzahrani

Waleed Al-Nuaimy



Abstract

<jats:p>Acne vulgaris is the common form of acne that primarily affects adolescents, characterised by an eruption of inflammatory and/or non-inflammatory skin lesions. Accurate evaluation and severity grading of acne play a significant role in precise treatment for patients. Manual acne examination is typically conducted by dermatologists through visual inspection of the patient skin and counting the number of acne lesions. However, this task costs time and requires excessive effort by dermatologists. This paper presents automated acne counting and severity grading method from facial images. To this end, we develop a multi-scale dilated fully convolutional regressor for density map generation integrated with an attention mechanism. The proposed fully convolutional regressor module adapts UNet with dilated convolution filters to systematically aggregate multi-scale contextual information for density maps generation. We incorporate an attention mechanism represented by prior knowledge of bounding boxes generated by Faster R-CNN into the regressor model. This attention mechanism guides the regressor model on where to look for the acne lesions by locating the most salient features related to the understudied acne lesions, therefore improving its robustness to diverse facial acne lesion distributions in sparse and dense regions. Finally, integrating over the generated density maps yields the count of acne lesions within an image, and subsequently the acne count indicates the level of acne severity. The obtained results demonstrate improved performance compared to the state-of-the-art methods in terms of regression and classification metrics. The developed computer-based diagnosis tool would greatly benefit and support automated acne lesion severity grading, significantly reducing the manual assessment and evaluation workload.</jats:p>

Journal Article Type Article
Acceptance Date Feb 18, 2022
Online Publication Date Feb 23, 2022
Publication Date 2022-03
Publicly Available Date Mar 28, 2024
Journal Computers
Electronic ISSN 2073-431X
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 3
Article Number ARTN 31
Pages 31 - 31
DOI https://doi.org/10.3390/computers11030031
Keywords acne diagnosis; deep learning; density map generation; attention network; regression models; Faster-RCNN
Publisher URL https://www.mdpi.com/2073-431X/11/3/31

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