Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.
翻译:近8 000万美国人因变老、压力、药物或基因化而染发。发型和头皮相关疾病一开始往往被忽视。有时,病人无法区分发型和正常发型。诊断发型相关疾病耗时,因为需要专业皮肤学家进行视觉和医学测试。因此,总体诊断延迟,使病情更加严重。由于图像处理能力,不同部门,特别是医疗和健康信息学部门,使用神经网络应用来预测癌症和肿瘤等致命疾病。这些应用帮助临床医生和病人,并初步了解早期症状。在这项研究中,我们采用了一种深刻的学习方法,成功地预测了三种主要类型的发型和头皮相关疾病:羊毛、皮肤病和淋巴病。然而,由于这方面的研究有限,缺乏正确的结果数据集,以及分散在互联网上的各种图像之间的差异程度,特别是健康和健康信息学,从各种来源获得150张图像,然后对早期的症状进行初步的洞察。我们通过解析、稳定性数据升级、提高和稳定性网络的准确性数据,我们随后又通过稳定性的数据更新了历史数据,从而稳定性数据,从而稳定地调整了网络的准确性数据。</s>