用优化的深度网络精准识别淋巴瘤亚型，准确率达99.33%
Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks
- 结合DenseNet201与DNN，用HHO算法优化参数
- 在15000张病理图像上测试准确率达99.33%
- 适合临床辅助诊断，提升癌症分型效率
淋巴瘤诊断，尤其是亚型区分，对有效治疗至关重要，但因组织病理图像中形态差异细微而面临挑战。本研究提出一种新型混合深度学习框架，结合DenseNet201进行特征提取，使用密集神经网络（DNN）进行分类，并通过哈里斯鹰优化（HHO）算法优化模型参数。模型在包含15,000张活检图像的数据集上训练，涵盖三种淋巴瘤亚型：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）。该方法在测试集上达到99.33%的准确率，显著提升准确性和模型可解释性。通过精确率、召回率、F1分数和ROC-AUC的综合评估，验证了模型的鲁棒性与临床应用潜力。该框架为肿瘤学中的诊断准确性与效率提升提供了可扩展解决方案。
原文摘要 · Abstract (English)
Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33\%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology.
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