Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue

Seo, Sunyong; Park, Jinho

doi:10.1155/2020/6908018

Cited by 17 publications

(14 citation statements)

References 31 publications

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“…Hair loss feature (HLF) extraction from the skull picture is used in this work [10] to avoid baldness and scalp self-diagnosis. Microscopy may be put on a smart device to retrieve HLF.…”

Section: • Shapementioning

confidence: 99%

“…To solve this, studies are currently being undertaken which show great accuracy with small AI models, but it is believed that it will still have a problem fitting into solutions or light portable devices. Furthermore, to construct a model that performs exceptionally well in a certain area, the engineer needs a huge quantity of dataset for a pre-processed picture, which allows us to create a picture using the feature as input dataset via this task [10].…”

Section: Introductionmentioning

confidence: 99%

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Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder Problem

2022

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Lots of women all over the globe are affected by thinning hair, and the number of females suffering from the disease is growing per year. Another important component in the development of thinning hair is genetics. One of the most important goals is to make a clinical condition. For example, in the area of medicine, categorization is critical since one of the primary goals of the doctor is to determine whether or not a patient suffers from an illness. Alopecia areata is a kind of chronic illness that causes baldness in the affected region. AA may cause baldness for a variety of causes thus, testing may be essential to confirm if it is the source of the loss of hair. Machine learning approaches have shown promise in a variety of fields, including dermatology, and may be useful in identifying alopecia areata for better prediction and diagnosis. Proper detection of an illness is also influenced by the fluctuating character of illness signs. Deep learning algorithms for identifying hair loss levels in males using facial pictures in this research. In this situation, a special training database, including face photos with varying degrees of baldness, has been generated. Furthermore, despite the limited accessibility of hairs in such images, a matching approach for mechanically categorizing face images to design categorization tables of male baldness from the medical field is provided. The outcomes of the experiments demonstrate the potential and efficiency for medical, security, and business apps. Related work in machine learning for hair illness categorization has also been addressed. The main objective of this study to analyze several machine learning and deep learning strategies for the identification of alopecia as well as in humans, as well as to determine the accuracy of extracting features methodologies.

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“…Hair loss feature (HLF) extraction from the skull picture is used in this work [10] to avoid baldness and scalp self-diagnosis. Microscopy may be put on a smart device to retrieve HLF.…”

Section: • Shapementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder Problem

2022

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“…A trichoscopy method was proposed that involved extraction of hair loss feature by processing of scalp images using encapsulated techniques such as grid line selection and eigenvalue. The system was novel in terms of using a combination of computer vision and image processing techniques for alopecia areata diagnosis [14]. In another study, an automated classification method for the early diagnosis and treatment of alopecia was proposed using artificial neural networks (ANN).…”

Section: Related Workmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Shakeel

Khan

Chaudhry

et al. 2021

Computational and Mathematical Methods in Medicine

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Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T -test showed significant differences between the two accuracies with a p < 0.001 . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.

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“…More recently, hair loss features were extracted by combining a scalp image taken from the microscope using grid line selection and eigenvalue to determine the progression of hair loss [ 19 ]. Further, a support vector machine (SVM) and a k-nearest neighbor (KNN) were utilized to train a machine learning model to classify healthy and hair loss conditions [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN

Kim

Kwon²,

et al. 2022

J. Imaging

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Early and accurate detection of scalp hair loss is imperative to provide timely and effective treatment plans to halt further progression and save medical costs. Many techniques have been developed leveraging deep learning to automate the hair loss detection process. However, the accuracy and robustness of assessing hair loss severity still remain a challenge and barrier for transitioning such a technique into practice. The presented work proposes an efficient and accurate algorithm to classify hair follicles and estimate hair loss severity, which was implemented and validated using a multitask deep learning method via a Mask R-CNN framework. A microscopic image of the scalp was resized, augmented, then processed through pre-trained ResNet models for feature extraction. The key features considered in this study concerning hair loss severity include the number of hair follicles, the thickness of the hair, and the number of hairs in each hair follicle. Based on these key features, labeling of hair follicles (healthy, normal, and severe) were performed on the images collected from 10 men in varying stages of hair loss. More specifically, Mask R-CNN was applied for instance segmentation of the hair follicle region and to classify the hair follicle state into three categories, following the labeling convention (healthy, normal and severe). Based on the state of each hair follicle captured from a single image, an estimation of hair loss severity was determined for that particular region of the scalp, namely local hair loss severity index (P), and by combining P of multiple images taken and processed from different parts of the scalp, we constructed the hair loss severity estimation (Pavg) and visualized in a heatmap to illustrate the overall hair loss type and condition. The proposed hair follicle classification and hair loss severity estimation using Mask R-CNN demonstrated a more efficient and accurate algorithm compared to other methods previously used, enhancing the classification accuracy by 4 to 15%. This performance supports its potential for use in clinical settings to enhance the accuracy and efficiency of current hair loss diagnosis and prognosis techniques.

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Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue

Cited by 17 publications

References 31 publications

Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder Problem

Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder Problem

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

Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN

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