Methods of Transfer Learning for Multiclass Hair Disease Categorization

Опубликована: Дек. 11, 2023

In the field of dermatology, skin disorders, particularly hair-related conditions, present a significant challenge. Image-based automated categorization hair problems has gained research attention due to its potential assist dermatologists in process early diagnosis and treatment planning. Transfer learning, technique that utilizes pre-trained deep neural networks, proven be valuable various computer vision applications. This study investigates application transfer learning for leveraging multiclass classification disorders by utilizing three commonly used Convolutional Neural Network (CNN) architectures: AlexNet, VGG16, ResNet50. begins with collection comprehensive dataset comprising high-resolution images including but not limited alopecia areata, tinea capitis, androgenetic alopecia. Categorizing into different groups based on type severity each condition enables thorough evaluation models. approach is employed fine-tuning these network architectures using disease dataset. A hyperparameter tuning strategy also adopted optimize parameters such as rates, batch sizes, optimization methods enhance model performance. The results reveal all architectures, ResNet50, achieve 99% accuracy rate classifying diseases. Such technology their clinical practice enabling rapid precise detection, thereby improving patient outcomes healthcare efficiency. Further could explore integration models workflows telemedicine remote consultation.

Язык: Английский

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Deep-Learning-Based Scalp Image Analysis Using Limited Data

Electronics, Год журнала: 2023, Номер 12(6), С. 1380 - 1380

Опубликована: Март 14, 2023

The World Health Organization and Korea National Insurance assert that the number of alopecia patients is increasing every year, approximately 70 percent adults suffer from scalp problems. Although a genetic problem, it difficult to diagnose at an early stage. deep-learning-based approaches have been effective for medical image analyses, challenging generate deep learning models detection analysis because creating dataset challenging. In this paper, we present approach generating model specialized achieves high accuracy by applying data preprocessing, augmentation, ensemble analyses. We use containing 526 good, 13,156 mild, 3742 moderate, 825 severe images. was further augmented normalization, geometry-based augmentation (rotate, vertical flip, horizontal crop, affine transformation), PCA augmentation. compare performance single using ResNet, ResNeXt, DenseNet, XceptionNet, ensembles these models. best result achieved when ResNet were combined achieve 95.75 F1 score 87.05.

Язык: Английский

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Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

Skin Research and Technology, Год журнала: 2024, Номер 30(4)

Опубликована: Март 28, 2024

Hair and scalp disorders present a significant challenge in dermatology due to their clinical diversity overlapping symptoms, often leading misdiagnoses. Traditional diagnostic methods rely heavily on expertise are limited by subjectivity accessibility, necessitating more advanced accessible tools. Artificial intelligence (AI) deep learning offer promising solution for accurate efficient diagnosis.

Язык: Английский

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Caffeine as an Active Ingredient in Cosmetic Preparations Against Hair Loss: A Systematic Review of Available Clinical Evidence

Healthcare, Год журнала: 2025, Номер 13(4), С. 395 - 395

Опубликована: Фев. 12, 2025

Background/Objectives: Hair loss (alopecia or effluvium) can significantly affect the self-esteem and psychosocial well-being of patients, resulting in a reduced quality life. It may herald systemic disease, nutritional deficiency, side effects pharmacotherapy. Current therapeutic options for hair are not always satisfactory be associated with considerable effects; therefore, new solutions still sought. Caffeine seems to an effective agent against thanks its stimulating on cell growth good penetration into follicle. The aim this study was systematically review published clinical trials topical caffeine preparations loss. Methods: We searched PubMed, Scopus, Web Science investigating efficacy products loss, until 29 November 2024. evidence assessed using GRADE classification. Results: query returned 1121 articles, which 9 ultimately met inclusion criteria. In total, 684 people androgenetic alopecia, excessive thinning were included these trials. all studies, conclusions favor treatment; however, level scientific medium 3 low 1, very remaining 5. Their major flaws lack randomization placebo control groups, as well information concentration products. Conclusions: Results from studies date suggest that safe Nevertheless, better-designed well-defined required ultimate statement. Commercial offered market nowadays worth try, but due incomplete data product information, outcomes guaranteed.

Язык: Английский

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Spatial dynamic analysis and thematic mapping of vulnerable communities to urban floods

Cities, Год журнала: 2023, Номер 145, С. 104735 - 104735

Опубликована: Дек. 11, 2023

Язык: Английский

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Prevalence and major risk factors of non-communicable diseases: a machine learning based cross-sectional study

EUREKA Health Sciences, Год журнала: 2023, Номер 3, С. 28 - 45

Опубликована: Авг. 7, 2023

The aim: study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Participants: 146 hospitalized adults both genders aged 18-93 participated this cross-sectional research. Methods: We collected demographic vital information from Bangladesh. checked physical parameters, including blood sugar, serum creatinine, pressure, presence or absence major diseases. Then we used descriptive statistical approaches explore NCDs based on gender age group. Afterwards, relationship between different NCD pairs with their combined effects was analyzed using hypothesis testing at a 95 % confidence level. Finally, random forest XGBoost machine learning algorithms are predict comorbidity underlying responsible factors. Result: Our observed relationships gender, groups, obesity, (DM, CKD, IBS, CVD, CRD, thyroid). most frequently reported cardiovascular issues (CVD), which present 83.56 all participants. CVD more common male Consequently, participants had higher pressure distribution than females. Diabetes mellitus (DM), other hand, did not have gender-based inclination. Both DM an age-based progression. showed that chronic respiratory illness frequent middle-aged younger elderly individuals. Based data, every one five obese. comorbidities found 31.5 population has only NCD, 30.1 two NCDs, 38.3 NCDs. Besides, 86.25 diabetic issues. All thyroid our CVD. Using t-test, CKD (p-value 0.061). Males under 35 years statistically significant 0.018). also association over 65 0.038). Moreover, there been Thyroid (P<0.05) for those below 35-65. two-way ANOVA test find interaction heart combination diabetes. RTI affected old. Among algorithms, produced highest accuracy, 69.7 %, detection. Random feature importance detected age, weight waist-hip ratio as behind comorbidity. Conclusion: helps identify future risks vulnerable groups. By initiating implementing control plans study, it is possible reduce burden

Язык: Английский

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Hair Loss Stage Prediction Using Deep Learning

Опубликована: Фев. 9, 2024

Hair losses diseases are common and pose challenges in diagnosing accurately promptly. Traditional diagnostic methods involve visual medical tests by dermatologists, leading to delays that worsen conditions. To address this, a deep learning solution using 2D convolutional neural network (CNN) was implemented, effectively predicting hair loss categories: alopecia, psoriasis, folliculitis. Challenges included limited dataset access diverse online images affecting model precision. Besides the model's success, created factors like SSB, Ageing, hierarchical characteristics, Stress, valuable for future research. This study's significance lies aiding timely identification via applications, benefiting both professionals individuals. The project's approach hinges on leveraging power of models discern intricate patterns within frontal facial images. utilization Convolutional Neural Networks (CNNs) will enable automatic extraction relevant information from images, hence enabling capture minute variations density, coverage, distribution. These learned features subsequently serve as foundation an accurate comprehensive classification system aligns with Hamilton-Norwood scale's progressive stages loss. sum up, goal this research study is show techniques can automatically identify different photos. Through integration networks image processing skills, aims further development diagnosis treatment approaches, ultimately improving lives those who suffer illness.

Язык: Английский

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Quantitative analysis and development of alopecia areata classification frameworks

Опубликована: Янв. 1, 2024

Alopecia areata is an autoimmune disorder resulting in rapid and unpredictable hair loss on the scalp or body as immune system mistakenly attacks human follicles. In United States alone, about 6.7 million people experience a form of Alopecia. Early identification condition has shown notable potential improving treatment outcomes reducing complications. To diagnose Alopecia, researchers have proposed use deep learning (DL) techniques to classify images healthy alopecia-affected, which high potential. However, research implementing relevant DL algorithms field detection estimation limited. This paper presents comparative analysis our two newly optimized Convolutional neural networks (CNN) with other existing models. For training, we considered datasets comprised alopecia-affected hair. Due data unavailability, gathered from distinct datasets: one Figaro1k independently created dataset. After training algorithms, performed contrastive assessment determine most effective based criteria. We hypothesized that initial performance base network would be closely connected subsequent accuracy algorithm when it for new task. As expected, modified Inception-Resnet-v2 model achieved greatest performance, validation 97.94% 10.4%, respectively. The experimental results indicated serves framework Areata classification.

Язык: Английский

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The prediction of hairfall pattern in a person using artificial intelligence for better care and treatment

Опубликована: Фев. 21, 2024

Alopecia, also known as hair loss, is a term used to describe loss from the scalp or other parts of body. It can be caused by number factors, such genetics, hormonal fluctuations, illness external stressors. The five typical types are male pattern baldness, which typically hereditary and hormonal, characterized receding hairline thinning crown, then female usually causes general without noticeable hairline, especially crown. Genetic factors involved. Next comes Alopecia Areata, results in round bald spots sudden patchy that immune system has targeted follicle. Then there Telogen Effluvium, denotes temporary illness, stress resulting seam area. Finally, Traction gradually fall out tight combs constant pulling. This study aims utilize AI-related methods predict these above mentioned patterns.

Язык: Английский

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

Journal of Intelligent & Fuzzy Systems, Год журнала: 2023, Номер 45(6), С. 11369 - 11380

Опубликована: Окт. 3, 2023

Alopecia Areata (AA) is one of the most widespread diseases, which generally classified and diagnosed by Computer Aided Diagnosis (CAD) models. Though it improves AA diagnosis, has limited interoperability needs skilled radiologists in medical image interpretation. This problem can be solved developing Deep Learning (DL) models with CAD for accurately diagnosing patients. Many studies engaged only specific DL such as Convolutional Neural Network (CNN) imaging, provides different independent results many parameters, limits their generalizability datasets. To combat this limitation, work proposes an Ensemble Pre-Learned Optimized Long Short-Term Memory (EPL-OLSTM) model classification. Initially, healthy scalp hair images are separately fed to pre-learned CNN structures, i.e. AlexNet, ResNet, InceptionNet extract deep features. Then, these features passed OLSTM, Battle Royale Optimization (BRO) algorithm applied optimize LSTM’s hyperparameters. Moreover, output LSTM fuzzy-softmax into associated classes, including mild, moderate, severe. Thus, increase accuracy differentiating between multiple classes. Finally, extensive experiment using Figaro1k (for images) DermNet datasets demonstrates that EPL-OLSTM achieves 93.1% compared state-of-the-art

Язык: Английский

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