Dataset Enhancement in Hair Follicle Detection: ESENSEI Challenge

Jakubik, Jan

doi:10.15439/2018f388

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…However, ScalpEye requires a larger image than those in other hair-related studies and, although the hair follicles are detected, the number of hairs present in the follicles is unknown. Jakubík et al [5] preprocessed training and test datasets through axis conversion and rotation. They applied convolution layers, a rectified linear-unit activation function, and a pooling layer for dimension reduction for a detection model.…”

Section: Related Workmentioning

confidence: 99%

“…For hair transplantation, hairs in the occipital donor area are typically removed and transplanted to the hair-loss areas, which requires the hairs in the occipital donor area to be counted to determine the available contribution [2,3]. This hair-density measurement (HDM) process, performed manually by doctors, is time consuming and requires a high level of expertise to make an accurate diagnosis [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Kim

Kang

Lee

2022

Sensors

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Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Kim

Kang

Lee

2022

Sensors

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“…19 Deep learning approaches have also been applied for hair analysis. 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.…”

Section: Ai-assisted Hair Growth Analysismentioning

confidence: 99%

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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“…Therefore, not only finding the location of the hair follicle is a challenge, but also identifying the different types of hair follicles. In 2018, there was a competition to locate the position of hair follicles, and a team used a method to enhance the accuracy of detection 6 . However, there is less research on the detection of hair follicle classes.…”

Section: Introductionmentioning

confidence: 99%

A challenge of deep‐learning‐based object detection for hair follicle dataset

Chen

Zeng

et al. 2023

J of Cosmetic Dermatology

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Background Deep‐learning object detection has been applied in various industries, including healthcare, to address hair loss. Methods In this paper, YOLOv5 object detection algorithm was used to detect hair follicles in a small and specific image dataset collected using a specialized camera on the scalp of individuals with different ages, regions, and genders. The performance of YOLOv5 was compared with other popular object detection models. Results YOLOv5 performed well in the detection of hair follicles, and the follicles were classified into five classes based on the number of hairs and the type of hair contained. In single‐class object detection experiments, a smaller batch size and the smallest YOLOv5s model achieved the best results, with an map of 0.8151. In multiclass object detection experiments, the larger YOLOv5l model was able to achieve the best results, and batch size affected the result of model training. Conclusion YOLOv5 is a promising algorithm for detecting hair follicles in a small and specific image dataset, and its performance is comparable to other popular object detection models. However, the challenges of small‐scale data and sample imbalance need to be addressed to improve the performance of target detection algorithms.

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Dataset Enhancement in Hair Follicle Detection: ESENSEI Challenge

Cited by 5 publications

References 3 publications

Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

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

A challenge of deep‐learning‐based object detection for hair follicle dataset

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