Pre-trained classification of scalp conditions using image processing

Abstract

<span>Scalp problems may occur due to the miscellaneous factor, which includes genetics, stress, abuse and hair products. The conventional technique for scalp and hair treatment involves high operational cost and complicated diagnosis. Besides, it is becoming progressively important for the payer to investigate the value of new treatment selection in the management of a specific scalp problem. As they are generally expensive and inconvenient, there is an increasing need for an affordable and convenient way of monitoring scalp conditions. Thus, this paper presents a study of pre-trained classification of scalp conditions using image processing techniques. Initially, the scalp image went through the pre-processing such as image enhancement and greyscale conversion. Next, three features of color, texture, and shape were extracted from each input image, and stored in a Region of Interest (ROI) table. The knowledge of the values of the pre-trained features is used as a reference in the classification process subsequently. A technique of Support Vector Machine (SVM) is employed to classify the three types of scalp conditions which are alopecia areata (AA), dandruff and normal. A total of 120 images of the scalp conditions were tested. The classification of scalp conditions indicated a good performance of 85% accuracy. It is expected that the outcome of this study may automatically classify the scalp condition, and may assist the user on a selection of suitable treatment available.</span>

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach

“…In another work, scalp images were classified according to three scalp conditions namely, alopecia areata, dandruff, and normal hair. The classification yielded an accuracy of 85% [16]. In another study, texture analysis was executed on scalp images using Severity of Alopecia Tool (SALT) score.…”

“…Previous work has been carried out with dermoscopic and scalp images. Similar image preprocessing steps were used in [16]; however, the study made use of scalp images and applied only SVM with 85% accuracy. Furthermore, feature extraction techniques were also different in [16] as compared to our proposed framework.…”

“…Similar image preprocessing steps were used in [16]; however, the study made use of scalp images and applied only SVM with 85% accuracy. Furthermore, feature extraction techniques were also different in [16] as compared to our proposed framework. Hence, our work demonstrates a novel and innovative framework for classifying alopecia areata using color, texture, and shape as features and SVM and KNN as classification algorithms.…”

“…Scalp analysis systems have been developed utilizing SVM and KNN to classify scalp images. Scalp images have been used for classification of conditions such as dandruff with the employment of machine learning techniques of SVM, KNN, and decision trees [14][15][16][17][18][19][20]. All these techniques use scalp and/or skin images to develop prediction models.…”

Feature extraction to predict quality of segregating sweet tamarind using image processing

“…For example, image classification algorithms are used to learn and classify data. The algorithms, such as k-nearest neighbors (KNN) [9], decision tree [10], random forest [11], support vector machine (SVM) [12], and logistic regression [13] were used. In addition, selecting the most appropriate data classification algorithm to extract the distinctive features of the images in the dataset is crucial.…”