Revolutionizing Lymphoma Diagnosis with Deep Learning and Natural Language Generation
M. Mohana, R. Rithika, V. Sivasakthi · 2024
The diagnostic complexities of malignant lymphoma, which comprise many subtypes like CLL, FL, and MCL, provide a daunting oncology challenge. Expert pathologists struggle with the widespread specter of staining variability, which results in significant diagnostic incongruity, when faced with the demands of differential diagnosis from Hematoxylin/Eosin (H+E)-stained biopsy specimens. By combining a creative synergy of cutting-edge convolutional neural networks (CNNs), such as VGG19, ResNet50v2, MobileNetV3, and DenseNet201, within an ensemble architecture, this research aims to deliver a ground-breaking solution. This combination gives researchers an unmatched ability to explain the subtle differences between malignant lymphoma subtypes and provide insightful prognostic information. The heterogeneous reservoir of biopsy samples that was painstakingly vetted by a group of pathologists from various clinical specialties serves as the empirical basis for our inquiry. By carefully utilizing the many benefits that each individual CNN possesses, the ensemble design significantly improves classification accuracy when compared to monolithic models. Furthermore, by strategically using GPT-2, a leading natural language processing model, this initiative goes beyond traditional diagnostic paradigms. This study announces a significant development in the field of malignant lymphoma diagnosis, promising more accurate and insightful evaluations, ultimately lightening the load on seasoned pathologists and improving patient care. Broader data source integration and the application of this paradigm to a variety of oncological entities are potential research directions.