Deep learning approach to detect malaria from microscopic images

• Published in: Multimedia tools and applications Vol. 79; no. 21-22; pp. 15297 - 15317
• Main Authors: Vijayalakshmi, A, Rajesh, Kanna B
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Digital imaging; Feature extraction; Image acquisition; Image analysis; Image classification; Image detection; Image processing; Infectious diseases; Machine learning; Malaria; Medical imaging; Multimedia Information Systems; Neural networks; Smear; Special Purpose and Application-Based Systems; State of the art; Support vector machines; Deep learning; VGG19; Malaria; Transfer learning; Convolutional neural network; VGG16; Support vector machine

Malaria is an infectious disease which is caused by plasmodium parasite. Several image processing and machine learning based techniques have been employed to diagnose malaria, using its spatial features extracted from microscopic images. In this work, a novel deep neural network model is introduced...