FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

Olimov, Bekhzod; Sanjar, Karshiev; Din, Sadia; Ahmad, A.E.; Paul, Anand; Kim, Jeonghong

doi:10.1007/s00530-020-00726-w

Cited by 29 publications

(16 citation statements)

References 41 publications

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“…The classification metrics were used to analyze the quality of prediction for each sign gesture, as shown in Tables 3 , 4 , and 5 , respectively. These qualities include precision, recall, and F1-score 66 – 68 , which are calculated using four values: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). We conducted experiments using different models as mentioned in Table 2 by inserting each model in the place of our proposed MOPGRU layers in the architecture presented in the paper.…”

Section: Resultsmentioning

confidence: 99%

An integrated mediapipe-optimized GRU model for Indian sign language recognition

Subramanian

Olimov

Naik

et al. 2022

Sci Rep

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Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.

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Section: Resultsmentioning

confidence: 99%

An integrated mediapipe-optimized GRU model for Indian sign language recognition

Subramanian

Olimov

Naik

et al. 2022

Sci Rep

Self Cite

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“…The semantic segmentation method based on deep learning automatically learns data features instead of using artificial data features, which is different from traditional image segmentation methods. End-to-end semantic segmentation prediction can be completed by using deep neural networks [21]. The three most important processes in deep learning include feature extraction, semantic segmentation and post-processing.…”

Section: Related Workmentioning

confidence: 99%

BiSeNet-oriented context attention model for image semantic segmentation

Teng

Qiao

2022

COMSIS J

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When the traditional semantic segmentation model is adopted, the different feature importance of feature maps is ignored in the feature extraction stage, which results in the detail loss, and affects the segmentation effect. In this paper, we propose a BiSeNet-oriented context attention model for image semantic segmentation. In the BiSeNet, the spatial path is utilized to extract more low-level features to solve the problem of information loss in deep network layers. Context attention mechanism is used to mine high-level implied semantic features of images. Mean while, the focus loss is used as the loss function to improve the final segmentation effect by reducing the internal weighting. Finally, we conduct experiments on open data sets, and the results show that pixel accuracy, average pixel accuracy, and aver age Intersection-over-Union are greatly improved compared with other state-of-the art semantic segmentation models. It effectively improves the accuracy of feature extraction, reduces the loss of feature details, and improves the final segmentation effect.

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“…There are several computational methods in the literature to perform the segmentation task. These methods can be classified as conventional methods and methods based on deep learning [14].…”

Section: Related Workmentioning

confidence: 99%

U-Net_based_Network_Applied_to_Skin_Lesion_Segmentation_An_Ablation_Study

Araújo

2022

CLEIej

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O câncer de pele é um dos cânceres que mais agrava os problemas de saúde pública. Dentre os tipos de câncer, o melanoma é o mais agressivo. Seu diagnóstico precoce é fundamental para aumentar a possibilidade de tratamento adequado, visando reduzir a taxa de mortalidade. Os dermatologistas geralmente usam métodos manuais para diagnosticar lesões de pele. Além de demorados por serem realizados manualmente, esses métodos podem apresentar resultados diferentes para a mesma lesão quando analisados ??por diferentes especialistas. Portanto, um diagnóstico automatizado pode ser necessário para lidar com esse problema e evitar testes invasivos. Segmentar a lesão cutânea na imagem dermatoscópica pode ser fundamental, pois se trata de umatarefa essencial no processo de análise de imagens. No presente trabalho, um modelo de Rede Neural Convolucional (CNN), baseado na U-Net, é proposto para segmentar a lesão em imagens dermatoscópicas . Este modelo obteve um Jaccard de 0,833 para a base ISIC 2017 e 0,850 para a base ISIC 2018. O modelo proposto possui uma arquitetura mais simples, exigindo menos recursos computacionais. Para verificar a eficácia do método proposto, realizamos um estudo de ablação e experimentos comparativos com outros métodos.

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FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

Cited by 29 publications

References 41 publications

An integrated mediapipe-optimized GRU model for Indian sign language recognition

An integrated mediapipe-optimized GRU model for Indian sign language recognition

BiSeNet-oriented context attention model for image semantic segmentation

U-Net_based_Network_Applied_to_Skin_Lesion_Segmentation_An_Ablation_Study

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