SAMMI: Segment Anything Model for Malaria Identification

Luca Zedda, Andrea Loddo, Cecilia Di Ruberto

2024

Abstract

Malaria, a life-threatening disease caused by the Plasmodium parasite, is a pressing global health challenge. Timely detection is critical for effective treatment. This paper introduces a novel computer-aided diagnosis system for detecting Plasmodium parasites in blood smear images, aiming to enhance automation and accessibility in comprehensive screening scenarios. Our approach integrates the Segment Anything Model for precise unsupervised parasite detection. It then employs a deep learning framework, combining Convolutional Neural Networks and Vision Transformer to accurately classify malaria-infected cells. We rigorously evaluate our system using the IML public dataset and compare its performance against various off-the-shelf object detectors. The results underscore the efficacy of our method, demonstrating superior accuracy in detecting and classifying malaria-infected cells. This innovative Computer-aided diagnosis system presents a reliable and near real-time solution for malaria diagnosis, offering significant potential for widespread implementation in healthcare settings. By automating the diagnosis process and ensuring high accuracy, our system can contribute to timely interventions, thereby advancing the fight against malaria globally.

Paper Citation

in Harvard Style

Zedda L., Loddo A. and Di Ruberto C. (2024). SAMMI: Segment Anything Model for Malaria Identification. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 367-374. DOI: 10.5220/0012325500003660

in Bibtex Style

@conference{visapp24,
author={Luca Zedda and Andrea Loddo and Cecilia Di Ruberto},
title={SAMMI: Segment Anything Model for Malaria Identification},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={367-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012325500003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - SAMMI: Segment Anything Model for Malaria Identification
SN - 978-989-758-679-8
AU - Zedda L.
AU - Loddo A.
AU - Di Ruberto C.
PY - 2024
SP - 367
EP - 374
DO - 10.5220/0012325500003660
PB - SciTePress