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Glossary Deep Learning

uses neural networks with many layers to analyze data

Deep Learning (DL) is a subset of machine learning that uses neural networks with many layers (hence "deep") to analyze and learn from large amounts of data. It mimics the way the human brain processes information, allowing it to recognize patterns, make decisions, and improve over time with more data. This technology is widely used in applications like image and speech recognition, natural language processing, and even in medical diagnostics, such as identifying patterns in hair loss conditions like alopecia.

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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 FRCNN based Deep Learning for Identification and Classification of Alopecia Areata

9 citations , February 2023

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.

research Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification

7 citations , October 2023 in “Journal of Intelligent & Fuzzy Systems”

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

research Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data

2 citations , January 2024

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

research Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers

1 citations , September 2025 in “Journal of Ultrasound in Medicine”

This study found that AI using YOLOv11 architecture can reliably differentiate between hyaluronic acid and silicone oil cosmetic fillers on ultrasound, achieving high accuracy, whereas identifying calcium hydroxyapatite and polymethylmethacrylate remains less consistent, requiring further improvements.

research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures

1 citations , November 2024

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

research Hair and scalp disease detection using deep learning

1 citations , March 2024 in “arXiv (Cornell University)”

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

research Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review

1 citations , August 2023 in “arXiv (Cornell University)”

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

1 citations , January 2023 in “IEEE access”

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks, and Clinical Features

research BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features

July 2026 in “International Journal of Advanced Research in Science Communication and Technology”

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

research Scalp Disease Detection Using Deep Learning: A Hybrid Ensemble Approach with EfficientNetB0, DenseNet121 and MobileNetV2

May 2026 in “International Journal of Scientific Research in Science and Technology”

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

research A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification

March 2026

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture

August 2025

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

research Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study

July 2025 in “Harvard Dataverse”

A deep learning model accurately detects early hair loss signs using scalp images.

Automated Early Detection of Androgenetic Alopecia Using Deep Learning on Trichoscopic Images From a Korean Cohort: A Retrospective Model Development and Validation Study

research Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study

July 2025 in “The Ewha Medical Journal”

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

research Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification

January 2025

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

research HairLossMultinet: A Multi Scale Feature Fusion Method Using Deep Learning Approach

January 2025 in “Communications in computer and information science”

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

research Enhanced Hair Disease Classification Using Deep Learning

March 2024

In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.

research Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment

January 2024 in “International Journal of Advanced Computer Science and Applications”

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

research Investigating Different Deep learning Models for Classification of Folliculitis

May 2023

This study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.

research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition

May 2023 in “Indian journal of science and technology”

This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.

research Deep Learning-based Automatic System for Diagnosis and Classification of Skin Dermatoses

December 2022 in “Research Square (Research Square)”

This study discusses the development of deep learning models for diagnosing skin disorders and notes challenges such as lack of data for darker skin tones, without providing new clinical results.

research An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning

January 2021 in “Lecture notes in networks and systems”

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

research Deep Learning Based Non-Invasive Framework for Nutritional Deficiency Detection Using Hair and Nail Images

March 2026 in “FMDB Transactions on Sustainable Health Science Letters”

This study developed a method using Convolutional Neural Networks to detect nutritional deficiencies, such as iron, zinc, biotin, and vitamins, through high-resolution images of hair and nails, achieving an 89% accuracy rate in identifying these deficiencies.

research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia

December 2021 in “Acta dermato-venereologica”

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

research DEEP REINFORCEMENT LEARNING FOR SCALABLE CONTROL OF BOOLEAN MODELS IN THE CONTEXT OF CELLULAR REPROGRAMMING

January 2024 in “Wiadomości Lekarskie”

In this study, researchers developed a novel computational framework using deep reinforcement learning to identify strategies for cellular reprogramming in gene regulatory networks, showing its effectiveness in a model of immune response against infection.

Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Network Architectures

research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures

1 citations , May 2025 in “Journal of Digital Information Management”

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

research PEDL-XAI: A Hybrid Probabilistic Ensemble Deep Learning Approach for Hair Disorder Diagnosis

April 2026 in “International Journal of Engineering Research and Science & Technology”

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

research A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY

September 2023 in “JP Journal of Biostatistics”

This study found that a random forest algorithm most effectively detected COVID-19, with high specificity and accuracy, among 10,862 individuals in an Iranian hospital setting.

research 200 Deep learning model to predict sentinel lymph node status in melanoma patients

April 2023 in “Journal of Investigative Dermatology”

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.