An Alternative Deep Learning Approach for Early Diagnosis of Malaria
- Title
- An Alternative Deep Learning Approach for Early Diagnosis of Malaria
- Creator
- Vrindavanam J.; Mary Jasmine E.; Kamath G.; Narayanan A.
- Description
- Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine Learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep Learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria. 2024 IEEE.
- Source
- 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- ConvNeXt; Deep Learning; Malaria detection; ResNet; Transfer Learning
- Coverage
- Vrindavanam J., Dayananda Sagar University, Department of Computer Science and Engineering(AI & ML), Bengaluru, India; Mary Jasmine E., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Kamath G., Dayananda Sagar University, Department of Computer Science and Engineering(AI & ML), Bengaluru, India; Narayanan A., Dayananda Sagar University, Department of Computer Science and Engineering(AI & ML), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835037024-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Vrindavanam J.; Mary Jasmine E.; Kamath G.; Narayanan A., “An Alternative Deep Learning Approach for Early Diagnosis of Malaria,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19054.