Convolutional Neural Networks Towards Facial Skin Lesions Detection

• URL: http://arxiv.org/abs/2402.08592v1
• Date: Tue, 13 Feb 2024 16:52:10 GMT
• Title: Convolutional Neural Networks Towards Facial Skin Lesions Detection
• Authors: Reza Sarshar, Mohammad Heydari, Elham Akhondzadeh Noughabi
• Abstract summary: This study contributes by providing a model that facilitates the detection of blemishes and skin lesions on facial images. The proposed method offers advantages such as simple architecture, speed and suitability for image processing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial analysis has emerged as a prominent area of research with diverse applications, including cosmetic surgery programs, the beauty industry, photography, and entertainment. Manipulating patient images often necessitates professional image processing software. This study contributes by providing a model that facilitates the detection of blemishes and skin lesions on facial images through a convolutional neural network and machine learning approach. The proposed method offers advantages such as simple architecture, speed and suitability for image processing while avoiding the complexities associated with traditional methods. The model comprises four main steps: area selection, scanning the chosen region, lesion diagnosis, and marking the identified lesion. Raw data for this research were collected from a reputable clinic in Tehran specializing in skincare and beauty services. The dataset includes administrative information, clinical data, and facial and profile images. A total of 2300 patient images were extracted from this raw data. A software tool was developed to crop and label lesions, with input from two treatment experts. In the lesion preparation phase, the selected area was standardized to 50 * 50 pixels. Subsequently, a convolutional neural network model was employed for lesion labeling. The classification model demonstrated high accuracy, with a measure of 0.98 for healthy skin and 0.97 for lesioned skin specificity. Internal validation involved performance indicators and cross-validation, while external validation compared the model's performance indicators with those of the transfer learning method using the Vgg16 deep network model. Compared to existing studies, the results of this research showcase the efficacy and desirability of the proposed model and methodology.

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