PDF(Pubmed)
Abstract：
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.
摘要：
恶性淋巴瘤,影响淋巴系统，由于其不同的亚型-慢性淋巴细胞白血病（CLL），在准确诊断方面提出了不同的挑战，滤泡性淋巴瘤（FL），套细胞淋巴瘤（MCL）。淋巴瘤是一种始于淋巴系统的癌症，影响淋巴细胞，是一种特殊类型的白细胞。本研究通过提出采用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解决这些挑战。对于合奏技术，本文采用基于堆栈的集成方法。这是一种两级分类方法，最适合提高准确性。在CLL的多类数据集上测试，FL,和MCL揭示了卓越的诊断准确性，DenseNet201、InceptionV3和Xception的准确率超过90%。提出的集成模型，利用InceptionV3和Xception，在300个周期内实现了出色的99%精度，超越以往的预测方法。这项研究证明了所提出的方法的可行性和效率，展示其在现实世界的精确诊断淋巴瘤的医疗应用的潜力。
