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Open AccessArticle

Liver Tumor Segmentation in CT Scans Using Modified SegNet

1
Department of Natural and Applied Sciences, Faculty of Community College, Majmaah University, Majmaah 11952, Saudi Arabia
2
Department of Biomedical Engineering, Higher Technological Institute, 10th Ramadan City 44629, Egypt
3
Department of Information Technology, College of Computer Sciences and Information Technology College, Majmaah University, Al-Majmaah 11952, Saudi Arabia
4
Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
5
Faculty of Media Engineering & amp; Technology, German University in Cairo, Cairo 11835, Egypt
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1516; https://doi.org/10.3390/s20051516
Received: 5 December 2019 / Revised: 26 February 2020 / Accepted: 4 March 2020 / Published: 10 March 2020
The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase. View Full-Text
Keywords: deep learning; CT images; convolutional neural networks; hepatic cancer deep learning; CT images; convolutional neural networks; hepatic cancer
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MDPI and ACS Style

Almotairi, S.; Kareem, G.; Aouf, M.; Almutairi, B.; Salem, M.A.-M. Liver Tumor Segmentation in CT Scans Using Modified SegNet. Sensors 2020, 20, 1516. https://doi.org/10.3390/s20051516

AMA Style

Almotairi S, Kareem G, Aouf M, Almutairi B, Salem MA-M. Liver Tumor Segmentation in CT Scans Using Modified SegNet. Sensors. 2020; 20(5):1516. https://doi.org/10.3390/s20051516

Chicago/Turabian Style

Almotairi, Sultan; Kareem, Ghada; Aouf, Mohamed; Almutairi, Badr; Salem, Mohammed A.-M. 2020. "Liver Tumor Segmentation in CT Scans Using Modified SegNet" Sensors 20, no. 5: 1516. https://doi.org/10.3390/s20051516

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