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

Kim, Jong-Hwan; Kwon, Segi; Fu, Jirui; Park, Joon‐Hyuk

doi:10.3390/jimaging8100283

Cited by 19 publications

(11 citation statements)

References 29 publications

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“…An effective and practical algorithm was developed for recognizing hair cells and measuring hair loss with the use of a mixed deep learning approach embedded inside a framework based on a Mask region‐based convolutional neural network (R‐CNN). 22 Hair loss severity was measured by counting hairs, measuring hair breadth, and counting the quantity of hairs. Ten males were photos at varying levels of hair loss, and using these criteria, the hair cells were classified as healthy, normal, or severe.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Chowdhury,

Sultan,

Jahan

et al. 2024

Skin Research and Technology

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BackgroundHair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are limited by subjectivity and accessibility, necessitating more advanced and accessible diagnostic tools. Artificial intelligence (AI) and deep learning offer a promising solution for more accurate and efficient diagnosis.MethodsThe research employs a modified Xception model incorporating ReLU activation, dense layers, global average pooling, regularization and dropout layers. This deep learning approach is evaluated against existing models like VGG19, Inception, ResNet, and DenseNet for its efficacy in accurately diagnosing various hair and scalp disorders.ResultsThe model achieved a 92% accuracy rate, significantly outperforming the comparative models, with accuracies ranging from 50% to 80%. Explainable AI techniques like Gradient‐weighted Class Activation Mapping (Grad‐CAM) and Saliency Map provided deeper insights into the model's decision‐making process.ConclusionThis study emphasizes the potential of AI in dermatology, particularly in accurately diagnosing hair and scalp disorders. The superior accuracy and interpretability of the model represents a significant advancement in dermatological diagnostics, promising more reliable and accessible diagnostic methods.

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Section: Literature Reviewmentioning

confidence: 99%

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

Chowdhury,

Sultan,

Jahan

et al. 2024

Skin Research and Technology

View full text Add to dashboard Cite

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“…Using the Road Event Awareness dataset [ 45 ], the efficacy of contrastive SSL approaches, such as BYOL and MoCo, was examined [ 46 ]. Mask R-CNN [ 47 ] was applied to instance segmentation and classification of the images [ 48 ]. The colorization task was integrated into BYOL in [ 49 ], and the resulting self-supervised method was trained on the cem500k dataset with two different encoders, namely Resnet50 and stand-alone self-attention.…”

Section: Related Workmentioning

confidence: 99%

Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired

et al. 2023

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This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user’s walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.

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“…Object detection algorithms have been used for the detection of scalp diseases such as hair follicles, and baldness detection. 11,12 However, only a few studies have been conducted using object detection algorithms for other sites of psoriasis. 13 When dermatologists diagnose SP using dermoscopy, the detection of lesion areas may be compromised by occlusion from scalp hair and the lesions being minimally detectable due to their small size, potentially leading to inaccuracies in determining the location and classification of these areas.…”

Section: Introductionmentioning

confidence: 99%

“…Object detection algorithms have been used for the detection of scalp diseases such as hair follicles, and baldness detection 11,12 . However, only a few studies have been conducted using object detection algorithms for other sites of psoriasis 13 .…”

Section: Introductionmentioning

confidence: 99%

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi‐Network Fusion Object Detection Framework for dermoscopic pattern diagnosis

Ji,

Li,

Zhu

et al. 2024

Skin Research and Technology

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BackgroundDermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported.ObjectivesEstablishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy.MethodsWe analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi‐network fusion object detection framework based on the object detection technique Faster R‐CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi‐network fusion object detection framework was compared with that between dermatologists.ResultsA total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi‐network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses.ConclusionsStudies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi‐network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

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

Cited by 19 publications

References 29 publications

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

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

Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi‐Network Fusion Object Detection Framework for dermoscopic pattern diagnosis

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