FRCNN Based Deep Learning for Identification and Classification of Alopecia Areata

February 2023

C. Saraswathi, B. Pushpa

TLDR The model accurately detects alopecia areata with 84.3% accuracy.

The study explored the use of FRCNN-based deep learning for the identification and classification of Alopecia Areata. Alopecia Areata is a condition characterized by hair loss, often linked to chronic stress and various lifestyle factors. The research aimed to improve the accuracy of diagnosing this condition through advanced machine learning techniques, potentially offering a more efficient and reliable method for healthcare professionals to identify and classify alopecia-related scalp issues.

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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)”

Trichoscopy helps diagnose and monitor the treatment of fungal scalp infections in children.

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

8 citations , January 2022 in “Sensors”

Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.

research Alopecia Areata: Current Understanding and Management

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

Alopecia areata is a chronic condition causing hair loss, with new treatments targeting the immune system showing promise.

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

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

Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.

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”

AI is effective in diagnosing and treating hair disorders, including detecting hair loss and scalp conditions with high accuracy, but it should supplement, not replace, doctor-patient interactions.

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”

Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.

research Pre-Trained Classification of Scalp Conditions Using Image Processing

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

The system can automatically classify scalp conditions with 85% accuracy.

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

74 citations , January 2020 in “IEEE Access”

ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.

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”

Trichoscopy helps diagnose hair and scalp disorders in dark-skinned people by identifying unique patterns.

research Alopecia Areata: A Comprehensive Review of Pathogenesis and Management

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

New treatments for Alopecia Areata show promise but need to be more effective and affordable.

research Hair Loss Diagnosis and Treatment Expert System

2 citations , January 2017

Minoxidil and Propecia are the only FDA-approved hair growth drugs, but people are exploring herbal alternatives due to concerns about side effects.

research Epidemiology and Burden of Alopecia Areata: A Systematic Review

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

Alopecia areata is a common autoimmune disease affecting about 2% of people, causing significant disability and often associated with mental health issues and other autoimmune conditions.

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”

Many patients at a dermatology clinic had scalp problems, often unrelated to why they visited, suggesting scalp checks should be regular.

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

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

Air pollution can cause hair loss, and using antioxidants, special shampoos, and coconut oil can improve scalp health and hair density.