Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Alternative Deep Learning Approach for Early Diagnosis of Malaria Subject The topic of the resource ConvNeXt; Deep Learning; Malaria detection; ResNet; Transfer Learning Description An account of the resource 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. Creator An entity primarily responsible for making the resource Vrindavanam J.; Mary Jasmine E.; Kamath G.; Narayanan A. Source A related resource from which the described resource is derived 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCCNT61001.2024.10724604" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT61001.2024.10724604</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165967&amp;doi=10.1109%2FICCCNT61001.2024.10724604&amp;partnerID=40&amp;md5=3b68d8d62ca43feed6aeb2ac341752c6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165967&amp;doi=10.1109%2fICCCNT61001.2024.10724604&amp;partnerID=40&amp;md5=3b68d8d62ca43feed6aeb2ac341752c6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037024-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vrindavanam J., Dayananda Sagar University, Department of Computer Science and Engineering(AI &amp; 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 &amp; ML), Bengaluru, India; Narayanan A., Dayananda Sagar University, Department of Computer Science and Engineering(AI &amp; ML), Bengaluru, India