Computational Models-Based Detection of Peripheral Malarial Parasites in Blood Smears. (8th June 2022)

• Record Type: Journal Article
• Authors: Alharbi, Amal H.; Aravinda, C. V.; Shetty, Jyothi; Jabarulla, Mohamed Yaseen; Sudeepa, K. B.; Singh, Sitesh Kumar
• Other Names: Tiwari Shailendra Academic Editor.
• Is Part Of: Contrast media & molecular imaging. Volume 2022(2022)
• Publication Date: 2022-06-08
• Subjects: Diagnostic imaging -- Periodicals; Magnetic resonance imaging -- Periodicals; Contrast media (Diagnostic imaging) -- Periodicals; Contrast Media -- Periodicals; Diagnostic Imaging -- Periodicals; Substances de contraste -- Périodiques; Diagnostics moléculaires -- Périodiques; Imagerie médicale; Substance de contraste; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.0754
• DOI: 10.1155/2022/9171343
• Languages: English
• ISSNs: 1555-4309

Abstract

Abstract : The most common human parasite as per the medical experts is the malarial disease, which is caused by a protozoan parasite, and Plasmodium falciparum, a common parasite in humans. A microscopist with expertise in malaria diagnosis must conduct this complex procedure to identify the stages of infection. This epidemic is an ongoing disease in some parts of the world, which is commonly found. A Kaggle repository was used to upload the data collected from the NIH portal. The dataset contains 27558 samples, of which 13779 samples carry parasites and 13779 samples do not. This paper focuses on two of the most common deep transfer learning methods. Unlike other feature extractors, VGG-19's fine-tuning and pretraining made it an ideal feature extractor. Several image classification models, including VGG-19, have been pretrained on larger datasets. Additionally, deep learning strategies based on pretrained models are proposed for detecting malarial parasite cases in the early stages, in addition to an accuracy rating of 98.34 ∗ 0.51%.