Semantic Segmentation of Venous on Deep Vein Thrombosis (DVT) Case using UNet-ResNet

Hernanda, Arta Kusuma; Purnama, I Ketut Eddy; Yuniarno, Eko Mulyanto; Nugroho, Johanes

doi:10.1109/icoict55009.2022.9914835

Cited by 4 publications

References 17 publications

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CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

Metlek

2024

Neural Comput & Applic

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Cell nucleus segmentation is an important method that is widely used in the diagnosis and treatment of many diseases, as well as counting and identifying the cell nucleus. The main challenges when using this method are heterogeneous image intensities in the image, overlapping of cell nuclei, and noise. In order to overcome these difficulties, a hybrid segmentation model with attention block, CellSegUNet, is proposed, inspired by the advantageous points of UNet++ and Residual UNet models. With the proposed attention mechanism, semantic gaps that may occur are prevented by evaluating both horizontal and vertical features together. The serial and parallel connection of the convolutional blocks in the residual modules in the CellSegUNet model prevents data loss. Thus, features with stronger representation ability were obtained. The output layer, which is, especially proposed for the CellSegUNet model, calculated the differences between the data in each layer and the data in the input layer. The output value obtained from the layer level where the lowest value comes from constitutes the output of the whole system. At the same depth level, CellSegUNet versus UNet++ and ResUNet models were compared on Data Science Bowl (DSB), Sartorius Cell Instance Segmentation (SCIS), and Blood Cell Segmentation (BCS) datasets. With the CellSegUNet model, accuracy, dice, and jaccard metrics were obtained as 0.980, 0.970, 0.959 for the DSB dataset, 0.931, 0.957, 0.829 for the SCIS dataset and 0.976, 0.971, 0.927 for the BCS dataset, respectively. As a result, it is predicted that the proposed model can provide solutions to different segmentation problems.

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CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

Metlek

2024

Neural Comput & Applic

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A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning

El-Latif,

El-dosuky,

Darwish

et al. 2024

Sci Rep

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Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide images. However, it is necessary to have an automated system that is more accurate and standardized for decision-making, which is essential for early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian cancer system is proposed. This model starts by extracting the main features from the histopathology images based on the ResNet-50 model to detect and classify the cancer. Then, recursive feature elimination based on a decision tree is introduced to remove unnecessary features extracted during the feature extraction process. Adam optimizers were implemented to optimize the network’s weights during training data. Finally, the advantages of combining deep learning and fuzzy logic are combined to classify the images of ovarian cancer. The dataset consists of 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. H&E-stained Whole Slide Images (WSIs), including 162 effective and 126 invalid WSIs were obtained from different tissue blocks of post-treatment specimens. Experimental results can diagnose ovarian cancer with a potential accuracy of 98.99%, sensitivity of 99%, specificity of 98.96%, and F1-score of 98.99%. The results show promising results indicating the potential of using fuzzy deep-learning classifiers for predicting ovarian cancer.

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Digital Twin Modeling and Transformer Prediction-Based Overall Particle Size Distribution Online Estimation

Liu,

Jiang,

Gui

et al. 2023

2023 China Automation Congress (CAC)

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Semantic Segmentation of Venous on Deep Vein Thrombosis (DVT) Case using UNet-ResNet

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Cited by 4 publications

References 17 publications

CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning

Digital Twin Modeling and Transformer Prediction-Based Overall Particle Size Distribution Online Estimation

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