The morbidity of scalp diseases is minuscule compared to other diseases, but the impact on the patient's life is enormous. It is common for people to experience scalp problems that include Dandruff, Psoriasis, Tinea-Capitis, Alopecia and Atopic-Dermatitis. In accordance with WHO research, approximately 70% of adults have problems with their scalp. It has been demonstrated in descriptive research that hair quality is impaired by impaired scalp, but these impacts are reversible with early diagnosis and treatment. Deep Learning advances have demonstrated the effectiveness of CNN paired with FCN in diagnosing scalp and skin disorders. In one proposed Deep-Learning-based scalp inspection and diagnosis system, an imaging microscope and a trained model are combined with an app that classifies scalp disorders accurately with an average precision of 97.41%- 99.09%. Another research dealt with classifying the Psoriasis using the CNN with an accuracy of 82.9%. As part of another study, an ML based algorithm was also employed. It accurately classified the healthy scalp and alopecia areata with 91.4% and 88.9% accuracy with SVM and KNN algorithms. Using deep learning models to diagnose scalp related diseases has improved due to advancements i computation capabilities and computer vision, but there remains a wide horizon for further improvements.

翻译：头皮疾病的发病率虽低于其他疾病，但对患者生活的影响极为显著。人们常见的头皮问题包括头皮屑、银屑病、头癣、脱发和特应性皮炎。根据世界卫生组织的研究，约70%的成年人存在头皮问题。描述性研究表明，头皮受损会导致发质损伤，但这些影响可通过早期诊断和治疗逆转。深度学习技术的进展已证实卷积神经网络与全卷积神经网络在头皮及皮肤疾病诊断中的有效性。在一项基于深度学习的头皮检测与诊断系统中，成像显微镜与训练模型结合应用，实现了97.41%-99.09%的平均精度对头皮疾病进行分类。另一项研究采用卷积神经网络对银屑病进行分类，准确率达82.9%。另有研究应用基于机器学习的算法，通过支持向量机和K近邻算法分别以91.4%和88.9%的准确率实现了健康头皮与斑秃的分类。得益于计算能力与计算机视觉的进步，利用深度学习模型诊断头皮相关疾病已取得显著进展，但未来仍有广阔提升空间。