A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images
The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and gradin...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/4/370 |
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| author | Saeed Iqbal Adnan N. Qureshi Musaed Alhussein Khursheed Aurangzeb Seifedine Kadry |
| author_facet | Saeed Iqbal Adnan N. Qureshi Musaed Alhussein Khursheed Aurangzeb Seifedine Kadry |
| author_sort | Saeed Iqbal |
| collection | DOAJ |
| description | The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model’s guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model’s superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities. |
| first_indexed | 2024-03-11T00:05:02Z |
| format | Article |
| id | doaj.art-0431b9ba48774a6c8c4562f6c0150a57 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T00:05:02Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-0431b9ba48774a6c8c4562f6c0150a572023-11-19T00:22:55ZengMDPI AGBiomimetics2313-76732023-08-018437010.3390/biomimetics8040370A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology ImagesSaeed Iqbal0Adnan N. Qureshi1Musaed Alhussein2Khursheed Aurangzeb3Seifedine Kadry4Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, PakistanDepartment of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, PakistanDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaDepartment of Applied Data Science, Noroff University College, 4612 Kristiansand, NorwayThe automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model’s guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model’s superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.https://www.mdpi.com/2313-7673/8/4/370bioinspirationmedical image analysistumor assessmentconvolutional neural network (CNN)heteromorphous deep CNNhistopathology images |
| spellingShingle | Saeed Iqbal Adnan N. Qureshi Musaed Alhussein Khursheed Aurangzeb Seifedine Kadry A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images Biomimetics bioinspiration medical image analysis tumor assessment convolutional neural network (CNN) heteromorphous deep CNN histopathology images |
| title | A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images |
| title_full | A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images |
| title_fullStr | A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images |
| title_full_unstemmed | A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images |
| title_short | A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images |
| title_sort | novel heteromorphous convolutional neural network for automated assessment of tumors in colon and lung histopathology images |
| topic | bioinspiration medical image analysis tumor assessment convolutional neural network (CNN) heteromorphous deep CNN histopathology images |
| url | https://www.mdpi.com/2313-7673/8/4/370 |
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