Hair Segmentation and Removal in Dermoscopic Images Using Deep Learning

Talavera-Martínez, Lidia; Bibiloni, Pedro; González-Hidalgo, Manuel

doi:10.1109/access.2020.3047258

Cited by 40 publications

(33 citation statements)

References 31 publications

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“…Another new direction in the use of NN for Me detection is the preprocessing tasks. An example of such a network can be seen in [117], a recently published paper that proposed an encoder-decoder CNN for hair removal (Figure 24).…”

Section: Melanoma Detection Using One Convolutional Neural Networkmentioning

confidence: 99%

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New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

Popescu

Elkhatib

El-Khatib

et al. 2022

Sensors

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Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it necessary to detect the early stages to be treated urgently and properly. This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases. The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence, especially neural network-based systems. Such structures can be considered intelligent support systems for dermatologists. Theoretical and applied contributions were investigated in the new development trends of multiple neural network architecture, based on decision fusion. The most representative articles covering the area of melanoma detection based on neural networks, published in journals and impact conferences, were investigated between 2015 and 2021, focusing on the interval 2018–2021 as new trends. Additionally presented are the main databases and trends in their use in teaching neural networks to detect melanomas. Finally, a research agenda was highlighted to advance the field towards the new trends.

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Section: Melanoma Detection Using One Convolutional Neural Networkmentioning

confidence: 99%

“…Figure24. The schematic architecture of the proposed system for hair removal from skin lesion images from[117].…”

mentioning

confidence: 99%

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

Popescu

Elkhatib

El-Khatib

et al. 2022

Sensors

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“…Contrary to the aforementioned methods, Talavera‐Martínez et al 22 . relied on deep learning to address this problem.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, these studies were validated on small datasets (20, 40, 50 images… etc.). Talavera‐Martínez et al 22 . tried out deep learning by proposing a convolutional encoder‐decoder architecture that consists of 12 layers.…”

Section: Experimental Evaluation and Analysismentioning

confidence: 99%

“…Generative models, a class of deep learning that is used to generate realistic‐looking samples showed great success in many fields including the problem of hair in dermoscopy images. Autoencoders (AEs) are a family of generative models that were used by researchers for hair removal 22 . However, the proposed models require paired samples for the training 22 that consist of the hair‐free image and its correspondent hair‐occluded image, which is difficult to acquire due to the unavailability of such datasets.…”

Section: Introductionmentioning

confidence: 99%

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Hair removal in dermoscopy images using variational autoencoders

Bardou

Bouaziz

et al. 2022

Skin Research and Technology

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Background: In recent years, melanoma is rising at a faster rate compared to other cancers. Although it is the most serious type of skin cancer, the diagnosis at early stages makes it curable. Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin. In the last few decades, digital imaging devices have made great progress which allowed capturing and storing high-quality images from these examinations. The stored images are now being standardized and used for the automatic detection of melanoma. However, when the hair covers the skin, this makes the task challenging. Therefore, it is important to eliminate the hair to get accurate results. Methods:In this paper, we propose a simple yet efficient method for hair removal using a variational autoencoder without the need for paired samples. The encoder takes as input a dermoscopy image and builds a latent distribution that ignores hair as it is considered noise, while the decoder reconstructs a hair-free image. Both encoder and decoder use a decent convolutional neural networks architecture that provides high performance. The construction of our model comprises two stages of training. In the first stage, the model has trained on hair-occluded images to output hair-free images, and in the second stage, it is optimized using hair-free images to preserve the image textures. Although the variational autoencoder produces hair-free images, it does not maintain the quality of the generated images. Thus, we explored the use of three-loss functions including the structural similarity index (SSIM), L1-norm, and L2-norm to improve the visual quality of the generated images. Results:The evaluation of the hair-free reconstructed images is carried out using tdistributed stochastic neighbor embedding (SNE) feature mapping by visualizing the distribution of the real hair-free images and the synthesized hair-free images. The conducted experiments on the publicly available dataset HAM10000 show that our method is very efficient.

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