Progressing Toward Smart Brain Hemorrhage Detection: Machine Learning-Based Advanced Medical Imaging Technologies

Jingya Li

2024

Abstract

In the rapidly evolving field of neuroscience, early and accurate detection of brain hemorrhage remains a significant challenge with profound implications for patient outcomes. The integration of Machine Learning (ML) techniques into diagnostic processes represents a promising frontier, offering the potential to revolutionize how brain hemorrhages are identified and treated, thereby reducing the associated morbidity and mortality rates. This review explores the application of ML in detecting brain hemorrhage. Recognizing the significance of early and accurate detection, the review outlines the general ML workflow encompassing data collection, preprocessing, model development, training, and evaluation. It delves into specific ML methods, including traditional algorithms like Support Vector Machines (SVM) and Random Forests, alongside deep learning approaches such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), assessing their strengths and limitations. The discussion highlights key challenges faced by ML in this context, such as the "black box" nature of models affecting interpretability, issues with generalization across diverse datasets, and concerns surrounding data privacy. Proposed solutions and future prospects are offered to address these challenges, emphasizing the potential of cascading models and the importance of integrating more complex modeling techniques for improved clinical efficacy. This review extensively discusses various machine learning algorithms and their application to brain hemorrhage detection, aiming to drive improvements in ML and foster the integration of computer-aided diagnosis (CAD) in medical imaging.

Paper Citation

in Harvard Style

Li J. (2024). Progressing Toward Smart Brain Hemorrhage Detection: Machine Learning-Based Advanced Medical Imaging Technologies. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 422-428. DOI: 10.5220/0012939500004508

in Bibtex Style

@conference{emiti24,
author={Jingya Li},
title={Progressing Toward Smart Brain Hemorrhage Detection: Machine Learning-Based Advanced Medical Imaging Technologies},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={422-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012939500004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Progressing Toward Smart Brain Hemorrhage Detection: Machine Learning-Based Advanced Medical Imaging Technologies
SN - 978-989-758-713-9
AU - Li J.
PY - 2024
SP - 422
EP - 428
DO - 10.5220/0012939500004508
PB - SciTePress