Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks
Lung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Ce...
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2357182 |
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| author | Tushar Nayak Nitila Gokulkrishnan Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose Swathi K. S. |
| author_facet | Tushar Nayak Nitila Gokulkrishnan Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose Swathi K. S. |
| author_sort | Tushar Nayak |
| collection | DOAJ |
| description | Lung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Cell Carcinoma account for nearly 80% of all lung cancer cases, which makes classifying the two subtypes of high importance. Proposed in this study is a data pre-processing pipeline for the H&E-stained lung biopsy images along with a customized EfficientNetB3-based Convolutional Neural Network employing spatial attention, trained on a public three-class lung cancer histopathological image dataset. The pre-processing pipeline employed before training, validation and testing helps enhance features of the histopathological images and removes biases due to stain variations for increased model robustness. The usage of a pre-trained CNN helps the deep learning model generalize better with the pre-trained weights, while the attention mechanism On three-fold validation, the classifier bagged accuracies of 0.9943 ± 0.0012 and 0.9947 ± 0.0018 and combined F1-Scores of 0.9942 ± 0.0042 and 0.9833 ± 0.0216 over the validation and testing data respectively. The high performance of the model combined with its computational efficiency could enable easy deployment of our model without necessitating infrastructure overhaul. |
| format | Article |
| id | doaj-art-5fed2de1c0d4403db2a4639e51618faf |
| institution | OA Journals |
| issn | 2331-1916 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj-art-5fed2de1c0d4403db2a4639e51618faf2025-08-20T02:11:17ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2357182Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networksTushar Nayak0Nitila Gokulkrishnan1Krishnaraj Chadaga2Niranjana Sampathila3Hilda Mayrose4Swathi K. S.5Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Social And Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaLung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Cell Carcinoma account for nearly 80% of all lung cancer cases, which makes classifying the two subtypes of high importance. Proposed in this study is a data pre-processing pipeline for the H&E-stained lung biopsy images along with a customized EfficientNetB3-based Convolutional Neural Network employing spatial attention, trained on a public three-class lung cancer histopathological image dataset. The pre-processing pipeline employed before training, validation and testing helps enhance features of the histopathological images and removes biases due to stain variations for increased model robustness. The usage of a pre-trained CNN helps the deep learning model generalize better with the pre-trained weights, while the attention mechanism On three-fold validation, the classifier bagged accuracies of 0.9943 ± 0.0012 and 0.9947 ± 0.0018 and combined F1-Scores of 0.9942 ± 0.0042 and 0.9833 ± 0.0216 over the validation and testing data respectively. The high performance of the model combined with its computational efficiency could enable easy deployment of our model without necessitating infrastructure overhaul.https://www.tandfonline.com/doi/10.1080/23311916.2024.2357182Adenocarcinomaconvolutional neural network EfficientNetB3histopathological imageslung cancersquamous cell carcinomaJin Zhongmin, Southwest Jiaotong University, China |
| spellingShingle | Tushar Nayak Nitila Gokulkrishnan Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose Swathi K. S. Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks Cogent Engineering Adenocarcinoma convolutional neural network EfficientNetB3 histopathological images lung cancer squamous cell carcinoma Jin Zhongmin, Southwest Jiaotong University, China |
| title | Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks |
| title_full | Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks |
| title_fullStr | Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks |
| title_full_unstemmed | Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks |
| title_short | Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks |
| title_sort | automated histopathological detection and classification of lung cancer with an image pre processing pipeline and spatial attention with deep neural networks |
| topic | Adenocarcinoma convolutional neural network EfficientNetB3 histopathological images lung cancer squamous cell carcinoma Jin Zhongmin, Southwest Jiaotong University, China |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2357182 |
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