Cargando…
An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification
The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imagi...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416893/ https://www.ncbi.nlm.nih.gov/pubmed/37568831 http://dx.doi.org/10.3390/diagnostics13152469 |
_version_ | 1785087886744354816 |
---|---|
author | Hamed, Esraa A.-R. Salem, Mohammed A.-M. Badr, Nagwa L. Tolba, Mohamed F. |
author_facet | Hamed, Esraa A.-R. Salem, Mohammed A.-M. Badr, Nagwa L. Tolba, Mohamed F. |
author_sort | Hamed, Esraa A.-R. |
collection | PubMed |
description | The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time. |
format | Online Article Text |
id | pubmed-10416893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104168932023-08-12 An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification Hamed, Esraa A.-R. Salem, Mohammed A.-M. Badr, Nagwa L. Tolba, Mohamed F. Diagnostics (Basel) Article The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time. MDPI 2023-07-25 /pmc/articles/PMC10416893/ /pubmed/37568831 http://dx.doi.org/10.3390/diagnostics13152469 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 Hamed, Esraa A.-R. Salem, Mohammed A.-M. Badr, Nagwa L. Tolba, Mohamed F. An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification |
title | An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification |
title_full | An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification |
title_fullStr | An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification |
title_full_unstemmed | An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification |
title_short | An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification |
title_sort | efficient combination of convolutional neural network and lightgbm algorithm for lung cancer histopathology classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416893/ https://www.ncbi.nlm.nih.gov/pubmed/37568831 http://dx.doi.org/10.3390/diagnostics13152469 |
work_keys_str_mv | AT hamedesraaar anefficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT salemmohammedam anefficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT badrnagwal anefficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT tolbamohamedf anefficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT hamedesraaar efficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT salemmohammedam efficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT badrnagwal efficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification AT tolbamohamedf efficientcombinationofconvolutionalneuralnetworkandlightgbmalgorithmforlungcancerhistopathologyclassification |