Attention Mechanism Guided Deep Regression Model for Acne Severity Grading

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