Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks
Lung and Colon cancer are the leading diseases of death and disability in humans caused by a combination of genetic diseases and biochemical abnormalities. If these are diagnosed in their early stages, they can not be spread in organs and negatively impact human life. Many deep-learning networks hav...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135538 |
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| author | Nazmul Shahadat |
| author_facet | Nazmul Shahadat |
| author_sort | Nazmul Shahadat |
| collection | DOAJ |
| description | Lung and Colon cancer are the leading diseases of death and disability in humans caused by a combination of genetic diseases and biochemical abnormalities. If these are diagnosed in their early stages, they can not be spread in organs and negatively impact human life. Many deep-learning networks have recently been proposed to detect and classify these malignancies. However, incorrect detection or misclassification of these fatal diseases can significantly affect an individual's health and well-being. This paper introduces a novel, cost-effective, and mobile-embedded architecture to diagnose and classify Lung squamous cell carcinomas and adenocarcinomas of the lung and colon from digital pathology images. Extensive experiment shows that our proposed modifications achieve 100% testing results for lung, colon, and lung-and-colon cancer detection. Our novel architecture takes around 0.65 million trainable parameters and around 6.4 million flops to achieve the best lung and colon cancer detection performance. Compared with the other results, our proposed architecture shows state-of-the-art performance. |
| format | Article |
| id | doaj-art-2a5cd9d6e7b540848ed030f94cd75462 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-2a5cd9d6e7b540848ed030f94cd754622025-08-20T01:52:19ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13553871917Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention NetworksNazmul Shahadat0https://orcid.org/0000-0003-3762-4901MrLung and Colon cancer are the leading diseases of death and disability in humans caused by a combination of genetic diseases and biochemical abnormalities. If these are diagnosed in their early stages, they can not be spread in organs and negatively impact human life. Many deep-learning networks have recently been proposed to detect and classify these malignancies. However, incorrect detection or misclassification of these fatal diseases can significantly affect an individual's health and well-being. This paper introduces a novel, cost-effective, and mobile-embedded architecture to diagnose and classify Lung squamous cell carcinomas and adenocarcinomas of the lung and colon from digital pathology images. Extensive experiment shows that our proposed modifications achieve 100% testing results for lung, colon, and lung-and-colon cancer detection. Our novel architecture takes around 0.65 million trainable parameters and around 6.4 million flops to achieve the best lung and colon cancer detection performance. Compared with the other results, our proposed architecture shows state-of-the-art performance.https://journals.flvc.org/FLAIRS/article/view/135538deep learninglung and colon cancerlung cancercolon cancerlung and colon cancer using deep learningconvolution neural network1-d cnncnnsqueeze and excitation network |
| spellingShingle | Nazmul Shahadat Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks Proceedings of the International Florida Artificial Intelligence Research Society Conference deep learning lung and colon cancer lung cancer colon cancer lung and colon cancer using deep learning convolution neural network 1-d cnn cnn squeeze and excitation network |
| title | Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks |
| title_full | Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks |
| title_fullStr | Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks |
| title_full_unstemmed | Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks |
| title_short | Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks |
| title_sort | lung and colon cancer histopathological image classification using 1d convolutional channel based attention networks |
| topic | deep learning lung and colon cancer lung cancer colon cancer lung and colon cancer using deep learning convolution neural network 1-d cnn cnn squeeze and excitation network |
| url | https://journals.flvc.org/FLAIRS/article/view/135538 |
| work_keys_str_mv | AT nazmulshahadat lungandcoloncancerhistopathologicalimageclassificationusing1dconvolutionalchannelbasedattentionnetworks |