PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs
Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/5/469 |
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| author | Sivashankari Rajadurai Kumaresan Perumal Muhammad Fazal Ijaz Chiranji Lal Chowdhary |
| author_facet | Sivashankari Rajadurai Kumaresan Perumal Muhammad Fazal Ijaz Chiranji Lal Chowdhary |
| author_sort | Sivashankari Rajadurai |
| collection | DOAJ |
| description | Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis. |
| first_indexed | 2024-04-25T00:33:41Z |
| format | Article |
| id | doaj.art-be56330a1c5b465b8d9a3989a7e65bf7 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-25T00:33:41Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-be56330a1c5b465b8d9a3989a7e65bf72024-03-12T16:41:52ZengMDPI AGDiagnostics2075-44182024-02-0114546910.3390/diagnostics14050469PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNsSivashankari Rajadurai0Kumaresan Perumal1Muhammad Fazal Ijaz2Chiranji Lal Chowdhary3School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, IndiaSchool of IT and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, AustraliaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, IndiaMalignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.https://www.mdpi.com/2075-4418/14/5/469malignant lymphomachronic lymphocytic leukemia (CLL)follicular lymphoma (FL)mantle cell lymphoma (MCL)transfer learningDenseNet201 |
| spellingShingle | Sivashankari Rajadurai Kumaresan Perumal Muhammad Fazal Ijaz Chiranji Lal Chowdhary PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs Diagnostics malignant lymphoma chronic lymphocytic leukemia (CLL) follicular lymphoma (FL) mantle cell lymphoma (MCL) transfer learning DenseNet201 |
| title | PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs |
| title_full | PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs |
| title_fullStr | PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs |
| title_full_unstemmed | PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs |
| title_short | PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs |
| title_sort | precisionlymphonet advancing malignant lymphoma diagnosis via ensemble transfer learning with cnns |
| topic | malignant lymphoma chronic lymphocytic leukemia (CLL) follicular lymphoma (FL) mantle cell lymphoma (MCL) transfer learning DenseNet201 |
| url | https://www.mdpi.com/2075-4418/14/5/469 |
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