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.

本研究解决了淋巴瘤亚型诊断中存在的挑战，提出了一种新型的混合深度学习框架，该框架结合了DenseNet201进行特征提取和基于哈里斯鹰优化算法的密集神经网络进行分类。研究结果显示，该模型在诊断准确率上达到了99.33%，显著提高了准确性和可解释性，具有临床应用的潜力。

基于密集神经网络的混合优化技术用于恶性淋巴瘤癌症的诊断