Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

TL;DR

This paper introduces a hybrid deep learning framework combining DenseNet201 and DNN, optimized with HHO, achieving over 99% accuracy in classifying lymphoma subtypes from biopsy images.

Contribution

The study presents a novel hybrid model optimized with Harris Hawks Optimization for improved lymphoma diagnosis accuracy and interpretability.

Findings

Achieved 99.33% testing accuracy

Demonstrated robustness with precision, recall, and F1-score

Potential for clinical adoption in oncology diagnostics

Abstract

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.