An ensemble of Deep Convolutional Neural Networks for Marking Hair Follicles on Microscopic Images

Podlodowski, Łukasz; Roziewski, Szymon; Nurzyński, Marek

doi:10.15439/2018f389

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…As shown by this study, the deeper the network, the probability of higher accuracy increases. This theory was also confirmed by another paper (20). However, another potential factor that may have led to the high performance with both algorithms is the feature extraction process.…”

Section: Discussionsupporting

confidence: 57%

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Quantitative analysis and development of alopecia areata classification frameworks

Dubey,

Morales

2024

J Emerg Invest

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

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Section: Discussionsupporting

confidence: 57%

“…To prevent overfitting, we used an 80-20% data split, with 80% of the total data used for training and 20% for validation. This type of data split generally yields the highest performance levels (20).…”

Section: Methodsmentioning

confidence: 99%

Quantitative analysis and development of alopecia areata classification frameworks

Dubey,

Morales

2024

J Emerg Invest

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“…A convolutional NN (CNN) was developed to detect hair follicles in scalp dermoscopy images with significant differences in skin and hair color, 33 and a deep CNN with 23 layers achieved better accuracy at predicting the location of hair follicles than the benchmark CNN. 20 These new models are still experimental, but they indicate that deep learning applications for hair detection will lead to improvements in current technology. E-Shaver Improved DullRazor: faster, removes light, thin hairs as well.…”

Section: Ai-assisted Hair Growth Analysismentioning

confidence: 99%

How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders

Gupta

Ivanova

Renaud

2021

Dermatologic Therapy

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Artificial intelligence (AI) applications in medicine are rapidly evolving. Deep learning diagnostic models that can accurately classify skin lesions have been developed. New AI applications are also starting to emerge in the hair restoration field. The objective was to review the current and future clinical applications of AI in hair restoration and hair disorder diagnosis. Current AI applications in hair restoration include fully automated systems for hair detection and hair growth measurement. New deep learningbased systems have been proposed for scalp diagnosis and automated hair loss measurements, including devices that can be used for self-diagnosis. Hair restoration experts should recognize the potential benefits and limitations of these emerging technologies as they become more readily available to both clinicians and patients.

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“…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 ]. The feasibility of automating the process of hair density assessment was tested by measuring the number of hairs from hair follicles and their type using deep learning-based object detection as well as other methods [ 21 ], such as VGG-16 [ 22 ], EfficientDet [ 23 ], YOLOv4 [ 24 ], and DetectoRS [ 25 ]. The common shortcomings of these approaches are prevalent false detections on the number of hairs, inaccuracy in detecting hair follicles, and lacking entire scalp-level hair loss severity estimation.…”

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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An ensemble of Deep Convolutional Neural Networks for Marking Hair Follicles on Microscopic Images

Cited by 9 publications

References 8 publications

Quantitative analysis and development of alopecia areata classification frameworks

Quantitative analysis and development of alopecia areata classification frameworks

How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders

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

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