Abstract
Acne scarring occurs in 95% of people with acne vulgaris due to collagen loss or gains when the body is healing the damages of the skin caused by acne inflammation. Accurate classification of acne scars is a vital factor in providing a timely, effective treatment protocol. Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. The pre-processed input images are fed into our proposed model, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ScarNet</i> , for deep feature map extraction. The optimizer, loss function, activation functions, filter and kernel sizes, regularization methods, and the batch size of the proposed architecture are tuned so that the classification performance is maximized while minimizing the computational cost. Experimental results demonstrate the feasibility of the proposed method with accuracy, specificity, and kappa score of 92.53%, 95.38%, and 76.7%, respectively.
Highlights
Acne scars originate in the tissue damage resulting from the inflammatory acne lesions and Vulgaris, which may need long-time treatment to be removed [1]
The performance of the ScarNet is compared with four conventional machine learning (ML)-based classifiers and five pre-trained models on our developed dataset
This paper presented an automated end-to-end Convolutional Neural Network (CNN)-based network for acne scar classification
Summary
Acne scars originate in the tissue damage resulting from the inflammatory acne lesions and Vulgaris, which may need long-time treatment to be removed [1]. As the first attempt, an automated computer-aided acne scar classification system is proposed based on a novel deep 19-layer CNN model, i.e., ScarNet. In this model, the activation function, optimization algorithm, loss function, kernel sizes, and batch size are adjusted to reduce the computational cost, such as model size, training parameters, and running time. Comparing the performance of ScarNet with four conventional machine learning (ML)-based classifiers, i.e., Decision Tree (DT), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest, and five pre-trained deep learning-based models, i.e., Inception-V3, MobileNet, RedNet-50, AlexNet, and VGG-16 on the developed dataset, our model attained competitive performance in acne scar classification with high accuracy and minimized computational cost.
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