Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
- Title
- Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
- Creator
- Mahesh Kumar A.S.; Maindola M.; Salis V.E.; Mitra A.; Janumala T.; Vijayan A.
- Description
- There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE.
- Source
- 2024 1st International Conference on Software, Systems and Information Technology, SSITCON 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- automated diagnosis; convolutional neural networks; malaria; plasmodium; thin blood smear
- Coverage
- Mahesh Kumar A.S., Maharaja Institute of Technology, Department of Electronics and Communication Engineering, Belawadi, India; Maindola M., Graphic Era Deemed to Be University, Department of Computer Science and Engineering, Dehradun, India; Salis V.E., Global Academy of Technology, Department of Information Science and Engineering, Bengaluru, India; Mitra A., Symbiosis Law School, Pune, Symbiosis International (Deemed University), Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR), Pune, India; Janumala T., R v College of Engineering, Department of Electronics and Instrumentation Engineering, Bengaluru, India; Vijayan A., Christ University, Department of Computer Science and Engineering, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035293-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mahesh Kumar A.S.; Maindola M.; Salis V.E.; Mitra A.; Janumala T.; Vijayan A., “Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/18984.