Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation

Sanjar, Karshiev; Olimov, Bekhzod; Kim, Jaeil; Kim, Jae-Soo; Paul, Anand; Kim, Jeonghong

doi:10.3390/app10103658

Cited by 20 publications

(8 citation statements)

References 32 publications

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“…Recent papers such as [58] used U-Net CNN for SL segmentation. As can be seen in Figure 20, U-Net has a "U" form, being composed of 23 convolutional layers.…”

Section: U-netmentioning

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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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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“…Recent papers such as [58] used U-Net CNN for SL segmentation. As can be seen in Figure 20, U-Net has a "U" form, being composed of 23 convolutional layers.…”

Section: U-netmentioning

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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“…[17][18][19][20][21][22][23][24][25] For better classification performance, accurate lesion area extraction is very important. [26][27][28][29][30] In this regard, Olimov et al 26 proposed an image segmentation model which provides better results by modifying the U-Net model. In another work, Olimov et al 27 proposed an efficient deep CNN model to extract lesion area using atrous and asymmetric convolution.…”

Section: Related Workmentioning

confidence: 99%

“…In this regard, CNN 15,16 quickly becomes a choice in examining dermoscopic images 17‐25 . For better classification performance, accurate lesion area extraction is very important 26‐30 . In this regard, Olimov et al 26 proposed an image segmentation model which provides better results by modifying the U‐Net model.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce trainable parameters residual skip connections are used in the encoding step. Sanjar et al 28 modify the U‐Net model by using bilinear interpolation for upsampling and a nonlinearity parametric rectified linear unit is used. Perez et al 17 studied the effectiveness of twelve CNN models in diagnosing melanoma over 11 public datasets.…”

Section: Related Workmentioning

confidence: 99%

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A customized deep learning framework for skin lesion classification using dermoscopic images

Sahoo

Dash

Mohapatra

2022

Computer Animation & Virtual

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Automated analysis of skin lesions in dermoscopy images has gained much attention due to its medical importance in the early detection of melanoma. Detection of lesions has become a challenge due to the strong visual similarity between benign and malignant skin lesions. In this research, a customized deep convolutional neural network (CNN) architecture has been designed to discriminate between benign and malignant lesions. The model is designed carefully with lesser convolution layers, fewer filters, and parameters to achieve better classification performance compared to pretrained VGG16, ResNet50, InceptionV3 models and, ensures state‐of‐the‐art performance. The proposed model is composed of nine trainable layers: eight convolution layers and one fully connected layer. The suggested framework is extensively evaluated on the benchmark ISIC 2016 challenge dataset. The effect of different input transformations over the dataset has been studied. For fair comparison, standard deep learning models such as VGG16, ResNet50, and InceptionV3 have been used for lesion classification using transfer learning approach. The memory requirement of the proposed model is reduced by 388, 68, and 63 times and FLOPs needed are lowered by 95%, 85%, and 84% compared to VGG16‐TrL, ResNet50‐TrL, and InceptionV3‐TrL, respectively. Results show that class balancing with external images improves classification performance.

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“…F ACIAL motion deblurring for a single image is a specific but critical branches of image deblurring, aimed at restoring a sharp image latent in a motion-blurred face image. Besides being visually unpleasant, blurry face images also degrade the performance of many facial-related computer vision tasks such as face detection [62], [73], [87], face recognition [14], [75], facial emotion recognition [80], [91], and face medical image segmentation [63]. Therefore, face deblurring studies in computer vision and image processing have received much attention.…”

Section: Introductionmentioning

confidence: 99%

Continuous Facial Motion Deblurring

2022

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We introduce a novel framework for continuous facial motion deblurring that restores the continuous sharp moment latent in a single motion-blurred face image via a moment control factor. Although a motion-blurred image is the accumulated signal of continuous sharp moments during the exposure time, most existing single image deblurring approaches aim to restore a fixed number of frames using multiple networks and training stages. To address this problem, we propose a continuous facial motion deblurring network based on GAN (CFMD-GAN), which is a novel framework for restoring the continuous moment latent in a single motion-blurred face image with a single network and a single training stage. To stabilize the network training, we train the generator to restore continuous moments in the order determined by our facial motion-based reordering process (FMR) utilizing domain-specific knowledge of the face. Moreover, we propose an auxiliary regressor that helps our generator produce more accurate images by estimating continuous sharp moments. Furthermore, we introduce a control-adaptive (ContAda) block that performs spatially deformable convolution and channel-wise attention as a function of the control factor. Extensive experiments on the 300VW datasets demonstrate that the proposed framework generates a various number of continuous output frames by varying the moment control factor. Compared with the recent single-tosingle image deblurring networks trained with the same 300VW training set, the proposed method show the superior performance in restoring the central sharp frame in terms of perceptual metrics, including LPIPS, FID and Arcface identity distance. The proposed method outperforms the existing single-to-video deblurring method for both qualitative and quantitative comparisons. In our experiments on the 300VW test set, the proposed framework reached 33.14 dB and 0.93 for recovery of 7 sharp frames in PSNR and SSIM, respectively.

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Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation

Cited by 20 publications

References 32 publications

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

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

A customized deep learning framework for skin lesion classification using dermoscopic images

Continuous Facial Motion Deblurring

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