HSV model‐based segmentation driven facial acne detection using deep learning. Issue 3 (8th July 2021)

• Record Type: Journal Article
• Authors: Yadav, Neha; Alfayeed, Sk Md; Khamparia, Aditya; Pandey, Babita; Thanh, Dang N. H.; Pande, Sagar
• Other Names: Gupta Deepak guestEditor.; Kose Utku guestEditor.; Castillo Oscar guestEditor.; Al‐Turjman Fadi guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-08
• Subjects: acne deep learning (DL) -- machine learning (ML) -- image processing (IP) -- CNN K‐means; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12760
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Acne is a skin disease mainly caused by bacteria, the hair follicles exposed to oil, and dying skin cells. These sometimes trigger whiteheads, blackheads or pimples, usually on the neck, arms, arm and back of shoulders. Acne in adolescents is the most severe, even though it affects people of any generation. Doctors can easily detect acne by seeing a patient's skin, but automatic acne detection is not easy for machines. Deep learning (DL) approaches have been quite successful for various aspects like classification and detection of objects in real life. This paper proposes an enhanced DL CNN model with the Leaky ReLU activation function. DermNet NZ's facial acne images dataset is used for the experiments. Three different techniques‐ K‐Means, Texture Analysis and HSV Model‐Based Segmentation, are applied for image segmentation to extract the acne region from skin images. After applying all the above image segmentation methods five times for each method, output images from K‐Means and HSV (5 + 5 images) are collected and combined with the dataset. Using that dataset, one SVM model using Scikit‐learn and two CNN models‐ one with the ReLU activation function and another with the LeakyReLU activation function, is trained. Out of these three models, the proposed CNN (LeakyReLU) model achieved a 97.54% accuracy.