Rajadurai Sivashankari, Perumal Kumaresan, Ijaz Muhammad Fazal, Chowdhary Chiranji Lal
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India.
School of IT and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia.
Diagnostics (Basel). 2024 Feb 21;14(5):469. doi: 10.3390/diagnostics14050469.
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.
恶性淋巴瘤会影响淋巴系统，由于其多种亚型——慢性淋巴细胞白血病（CLL）、滤泡性淋巴瘤（FL）和套细胞淋巴瘤（MCL），在准确诊断方面存在各种挑战。淋巴瘤是一种始于淋巴系统的癌症，会影响淋巴细胞，淋巴细胞是一种特定类型的白细胞。本研究通过提出采用来自VGG16、VGG19、DenseNet201、InceptionV3和Xception的预训练权重的集成和非集成迁移学习模型来应对这些挑战。对于集成技术，本文采用基于堆叠的集成方法。这是一种两级分类方法，最适合提高准确性。在CLL、FL和MCL的多类数据集上进行测试显示出卓越的诊断准确性，DenseNet201、InceptionV3和Xception的准确率超过90%。所提出的集成模型利用InceptionV3和Xception，在300个轮次上实现了高达99%的出色准确率，超过了先前的预测方法。这项研究证明了所提出方法的可行性和效率，展示了其在实际医学应用中进行精确淋巴瘤诊断的潜力。
