En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, th...
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MDPI AG
2023-04-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/5/1309 |
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| author | Suganeshwari G Jothi Prabha Appadurai Balasubramanian Prabhu Kavin Kavitha C Wen-Cheng Lai |
| author_facet | Suganeshwari G Jothi Prabha Appadurai Balasubramanian Prabhu Kavin Kavitha C Wen-Cheng Lai |
| author_sort | Suganeshwari G |
| collection | DOAJ |
| description | Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder–Decoder Network (En–DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly. |
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| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T03:55:31Z |
| publishDate | 2023-04-01 |
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| series | Biomedicines |
| spelling | doaj.art-3d4e1e6821ed485d9e5bebe83275729b2023-11-18T00:35:06ZengMDPI AGBiomedicines2227-90592023-04-01115130910.3390/biomedicines11051309En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer DiagnosisSuganeshwari G0Jothi Prabha Appadurai1Balasubramanian Prabhu Kavin2Kavitha C3Wen-Cheng Lai4School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, IndiaComputer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, IndiaDepartment of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Chengalpattu District, Kattankulathur 603203, Tamilnadu, IndiaDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, Tamil Nadu, IndiaBachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, TaiwanLiver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder–Decoder Network (En–DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly.https://www.mdpi.com/2227-9059/11/5/1309liver segmentationencoder–decoder networkgradational modular networkcomputed tomographycancer diagnosis |
| spellingShingle | Suganeshwari G Jothi Prabha Appadurai Balasubramanian Prabhu Kavin Kavitha C Wen-Cheng Lai En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis Biomedicines liver segmentation encoder–decoder network gradational modular network computed tomography cancer diagnosis |
| title | En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis |
| title_full | En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis |
| title_fullStr | En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis |
| title_full_unstemmed | En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis |
| title_short | En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis |
| title_sort | en denet based segmentation and gradational modular network classification for liver cancer diagnosis |
| topic | liver segmentation encoder–decoder network gradational modular network computed tomography cancer diagnosis |
| url | https://www.mdpi.com/2227-9059/11/5/1309 |
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