Balachandar Jeganathan and the Future of Automated Plasma Cell Detection in Cancer Care

Balachandar Jeganathan harnessed the power of YOLOv8 to craft an automated system that reimagines plasma cell segmentation, bringing new speed and precision to the diagnosis of multiple myeloma and giving doctors a sharper tool in the fight against this disease.

In a remarkable fusion of technology and medicine, Balachandar Jeganathan from the United States is pioneering efforts to streamline and improve the diagnosis of multiple myeloma. At the heart of his work is a project titled "Automated Plasma Cell Segmentation for Multiple Myeloma Diagnosis: A Deep Learning Approach Using a Novel Dataset." With a clear focus on hematopathology, Balachandar's research introduces a fully automated solution for detecting plasma cells in bone marrow aspirate slides, an essential, yet time-intensive, step in identifying this complex blood cancer.

Balachandar brings his medical device software expertise into AI-driven diagnostics for multiple myeloma. Working independently, he built a full deep learning pipeline with YOLOv8, from curating a custom dataset to fine-tuning model performance. His tool accurately segments plasma cells in bone marrow slides—streamlining a critical diagnostic step to provide faster, more consistent results and improve patient care.

Central to this breakthrough is a custom dataset Balachandar created in 2024, tailored for plasma cell segmentation. With precise preprocessing and bounding box extraction, he ensured clean inputs for training. Using metrics like precision, recall, and mAP, he achieved strong, practical results—and is now preparing a research paper to share his findings with the medical and AI communities.

His work on automated plasma cell segmentation is a major breakthrough in medical AI, creating a highly accurate diagnostic tool for multiple myeloma. Using YOLOv8 and a custom annotated dataset, he tackled challenges in detecting complex cells. Through careful optimization and rigorous training, he developed a model that raises the bar for precision and reliability, promising faster, more accurate diagnoses. He is a proud recipient of the 2025 TITAN Business Award in Information Technology for his innovation in AI and automation.