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8000万美国人因衰老、压力、药物或遗传因素而遭受脱发困扰。毛发及头皮相关疾病在初期常被忽视，患者有时难以区分病理性脱发与生理性脱发。诊断毛发疾病需专业皮肤科医生进行目视检查和医学检测，耗时较长，导致整体诊断延误，进而加重病情严重程度。基于图像处理能力，神经网络应用被广泛应用于医疗健康与健康信息学等领域，用于预测癌症、肿瘤等致命疾病。此类应用可为临床医生和患者提供辅助诊断，并呈现早期症状的初步洞察。本研究采用深度学习方法，成功预测了三种主要脱发及头皮相关疾病：斑秃、银屑病和毛囊炎。然而，该领域研究有限、缺乏合适的数据集以及互联网上图像间存在的显著差异，使任务充满挑战。我们从多种来源获取150张图像，通过去噪、图像均衡化、增强及数据平衡进行预处理，从而最大限度降低误差率。将处理后的数据输入二维卷积神经网络（CNN）模型后，训练准确率达96.2%，验证准确率为91.1%。斑秃、银屑病和毛囊炎的精确率与召回率分别为0.895、0.846和1.0。此外，我们为未来研究者构建了头皮图像数据集。