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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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Autores principales: G, Suganeshwari, Appadurai, Jothi Prabha, Kavin, Balasubramanian Prabhu, C, Kavitha, Lai, Wen-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216267/
https://www.ncbi.nlm.nih.gov/pubmed/37238979
http://dx.doi.org/10.3390/biomedicines11051309
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author G, Suganeshwari
Appadurai, Jothi Prabha
Kavin, Balasubramanian Prabhu
C, Kavitha
Lai, Wen-Cheng
author_facet G, Suganeshwari
Appadurai, Jothi Prabha
Kavin, Balasubramanian Prabhu
C, Kavitha
Lai, Wen-Cheng
author_sort G, Suganeshwari
collection PubMed
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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spelling pubmed-102162672023-05-27 En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis G, Suganeshwari Appadurai, Jothi Prabha Kavin, Balasubramanian Prabhu C, Kavitha Lai, Wen-Cheng Biomedicines Article 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. MDPI 2023-04-28 /pmc/articles/PMC10216267/ /pubmed/37238979 http://dx.doi.org/10.3390/biomedicines11051309 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
G, Suganeshwari
Appadurai, Jothi Prabha
Kavin, Balasubramanian Prabhu
C, Kavitha
Lai, Wen-Cheng
En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver 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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216267/
https://www.ncbi.nlm.nih.gov/pubmed/37238979
http://dx.doi.org/10.3390/biomedicines11051309
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