Paludism Diagnosis Using Deep Learning
- DOI
- 10.2991/978-94-6463-496-9_19How to use a DOI?
- Keywords
- Malaria; Paludism; Convolutional Neural Network; Artificial Intelligence; Deep Learning; Machine Learning; Image Processing
- Abstract
Medical experts rely on various methods to detect diseases, aiming to identify the presence of parasites in blood samples. This research focuses on crafting a sophisticated deep-learning model tailored for paludism detection, leveraging microscopic images of blood smears. By surpassing the constraints of conventional diagnostic methods, our model seeks to enhance the precision of malaria detection. Our used approache is convolutional neural networks (CNNs). Evaluation is conducted on a publicly accessible dataset of malaria-infected blood smears, affirming the effectiveness of our approach over existing techniques with achieving results in accuracy, precision, F1-score, specificity, recall, sensitivity, and AUC, with values of 0.9975, 0.9893, 0.9975, 0.9892, 0.9994, 0.98, and 0.9985, respectively.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Fadia Baissi AU - Elhadj Abdelkader Abdelbaki AU - Laid Kahloul AU - Amira Mohammedi AU - Asma Ammari PY - 2024 DA - 2024/08/31 TI - Paludism Diagnosis Using Deep Learning BT - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024) PB - Atlantis Press SP - 246 EP - 260 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-496-9_19 DO - 10.2991/978-94-6463-496-9_19 ID - Baissi2024 ER -