FRCNN Based Deep Learning for Identification and Classification of Alopecia Areata

February 2023

C. Saraswathi, B. Pushpa

Alopecia Areata hair loss FRCNN deep learning machine learning scalp issues bald spots

Studysummary This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer

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Research cited in this study

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research A Case Report of Tinea Capitis in Children: Utility of Trichoscopy

1 citations , March 2022 in “Berkala Ilmu Kesehatan Kulit dan Kelamin/Berkala ilmu kesehatan kulit dan kelamin (Periodical of dermatology and venerology)”

This case analysis found that trichoscopy is a valuable tool for diagnosing and monitoring treatment progress in tinea capitis by distinguishing between Microsporum and Trichophyton etiologies.

research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

8 citations , January 2022 in “Sensors”

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

research Alopecia Areata: Current Understanding and Management

11 citations , January 2022 in “Journal der Deutschen Dermatologischen Gesellschaft”

This review discusses current and emerging therapies for alopecia areata, highlights the challenges in managing this chronic disease, and emphasizes the importance of addressing its psychosocial impacts; it reports no new clinical results.

research Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach

8 citations , August 2021 in “Computational and Mathematical Methods in Medicine”

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

research How Good Is Artificial Intelligence at Solving Hairy Problems? A Review of AI Applications in Hair Restoration and Hair Disorders

4 citations , January 2021 in “Dermatologic Therapy”

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.

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

10 citations , September 2020 in “Computational and Mathematical Methods in Medicine”

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

research Pre-Trained Classification of Scalp Conditions Using Image Processing

15 citations , August 2020 in “Indonesian Journal of Electrical Engineering and Computer Science”

This study found that a pre-trained image processing technique accurately classified scalp conditions with 85% accuracy, suggesting potential for automated diagnosis and treatment selection.

research ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health

74 citations , January 2020 in “IEEE Access”

This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.

research Trichoscopy of Dark Scalp: A Review of Trichoscopic Features in Hair and Scalp Disorders in Dark-Skinned Individuals

18 citations , July 2018 in “Skin appendage disorders”

This review discusses trichoscopic features of hair disorders in individuals with dark skin phototypes and reports no new clinical results; the authors emphasize the need for more specific diagnostic criteria for these populations.

research Alopecia Areata: A Comprehensive Review of Pathogenesis and Management

99 citations , July 2017 in “Clinical Reviews in Allergy & Immunology”

This review noted that alopecia areata is an autoimmune disease impacting hair follicles and offered insights into its complex pathogenesis involving immune responses, while highlighting ongoing research into new treatments such as Janus kinase inhibitors and other immunomodulatory drugs.

research Hair Loss Diagnosis and Treatment Expert System

2 citations , January 2017

This abstract reviews existing hair loss treatments, highlighting FDA-approved drugs like Minoxidil and Finasteride while noting growing interest in herbal alternatives due to concerns about potential long-term side effects.

research Epidemiology and Burden of Alopecia Areata: A Systematic Review

144 citations , July 2015 in “Clinical, Cosmetic and Investigational Dermatology”

This review reports that alopecia areata affects about 2% of people globally and is linked with psychiatric and medical comorbidities, highlighting its significant disease burden.

Prevalence of Scalp Disorders Among Outpatients Attending Dermatology Department in a Semi-Urban Setup: A Cross-Sectional Observational Study

research Prevalence of Scalp Disorders Among Outpatients Attending Dermatology Department in a Semi-Urban Setup: A Cross-Sectional Observational Study

1 citations , May 2015 in “Journal of evolution of medical and dental sciences”

In this study, a notable number of dermatology outpatients were found to have incidental scalp findings, indicating the importance of examining the scalp during clinical evaluations.

research Understanding Hair Loss Due to Air Pollution and the Approach to Management

7 citations , January 2015 in “Hair therapy & transplantation”

This review discusses the impact of air pollution on hair loss and sensitive scalp syndrome, evaluating possible causes and management strategies, but reports no new clinical results.