Abstract
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.
Highlights
Acne vulgaris, or acne, is a skin condition in which dead skin cells and oil from the skin block hair follicles
Facial acne is most common during adolescence, but it can persist into adulthood
To tackle the aforementioned limitations, we developed a new computer-assisted image analysis approach to grade the severity of acne lesions called dilated UNet dense regressor guided by attention mechanism
Summary
Acne, is a skin condition in which dead skin cells and oil from the skin block hair follicles This skin condition is clinically featured by blackheads and whiteheads (open and closed comedones), small and tender red bumps (papules), white or yellow squeezable spots (pustules), cyst-like fluctuant swellings (cysts), and large painful red lumps (nodules). It usually affects areas of skin with a high number of oil glands, such as the face, chest, back and shoulders [1,2]. Facial acne is most common during adolescence, but it can persist into adulthood. After severe inflammatory acne, scarring inevitably occurs. The resultant facial appearance can cause anxiety, low self-esteem, and, in the worst-case scenario, depression or suicidal thoughts [3,4]
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