Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks
<|reference_start|>Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks: 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.<|reference_end|>
@article{aly2024diagnosis,
    title={Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques
  Based on Dense Neural Networks},
    author={Salah A. Aly, Ali Bakhiet, Mazen Balat},
    journal={arXiv preprint arXiv:2410.06974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06974},
    primaryClass={eess.IV cs.CV cs.LG}
}