MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909466260144128 |
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| author | Bala, Diponkor Karim, S M Rakib Ul Rasul, Rownak Ara |
| author_facet | Bala, Diponkor Karim, S M Rakib Ul Rasul, Rownak Ara |
| contents | Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the histopathological diagnosis, an important factor in cancer type identification. This research focuses on creating a high-efficiency deep-learning model for identifying lung and colon cancer from histopathological images. We proposed a novel approach based on a modified residual attention network architecture. The model was trained on a dataset of 25,000 high-resolution histopathological images across several classes. Our proposed model achieved an exceptional accuracy of 99.30%, 96.63%, and 97.56% for two, three, and five classes, respectively; those are outperforming other state-of-the-art architectures. This study presents a highly accurate deep learning model for lung and colon cancer classification. The superior performance of our proposed model addresses a critical need in medical AI applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification Bala, Diponkor Karim, S M Rakib Ul Rasul, Rownak Ara Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the histopathological diagnosis, an important factor in cancer type identification. This research focuses on creating a high-efficiency deep-learning model for identifying lung and colon cancer from histopathological images. We proposed a novel approach based on a modified residual attention network architecture. The model was trained on a dataset of 25,000 high-resolution histopathological images across several classes. Our proposed model achieved an exceptional accuracy of 99.30%, 96.63%, and 97.56% for two, three, and five classes, respectively; those are outperforming other state-of-the-art architectures. This study presents a highly accurate deep learning model for lung and colon cancer classification. The superior performance of our proposed model addresses a critical need in medical AI applications. |
| title | MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.17700 |