Enhancing Blood Cancer Diognosis with Data Driven Techniques

Balaji J., Sanjai M., Hemadharshini V., Gunavathi R., Gobisha K.

2025

Abstract

Blood is needed by the human body to carry essential nutrients, oxygen and immune cells. Leukemia and lymphoma are two deliverables that can be traced to abnormalities in blood cells. Blood cancers which include a range from leukemia to lymphomas and plasma cell diseases are among the most difficult to diagnose, yet early detection is critical for effective treatment and positive clinical outcomes. In this scenario, the invention of an integrated automated blood cancer detection system is essential. This pipeline classifies the blood sample pictures and classifies between Benign, Early, Pre and Pro cancer types using a pre- trained ResNet50 model. Images are pre-processed for it to comply with its input requirements, and then the model learns crucial aspects to predict accurately. The model produced the type of cancer and a confidence score, which is an indication of how likely the prediction is correct. The system developed using Keras as its deep learning framework and Streamlit as its user interface, provides a reliable and portable tool that effectively automates the picture processing procedure to aid with blood cancer identification. The implementation of these consequences allows doctors in clinics to diagnose faster and more accurately by providing consistent and accurate, classifications, while reducing manual work.

Paper Citation

in Harvard Style

J. B., M. S., V. H., R. G. and K. G. (2025). Enhancing Blood Cancer Diognosis with Data Driven Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 738-743. DOI: 10.5220/0013872000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Balaji J. and Sanjai M. and Hemadharshini V. and Gunavathi R. and Gobisha K.},
title={Enhancing Blood Cancer Diognosis with Data Driven Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={738-743},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013872000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Enhancing Blood Cancer Diognosis with Data Driven Techniques
SN - 978-989-758-777-1
AU - J. B.
AU - M. S.
AU - V. H.
AU - R. G.
AU - K. G.
PY - 2025
SP - 738
EP - 743
DO - 10.5220/0013872000004919
PB - SciTePress