Analysis Of Microscopic Medical Images: A Case Study On Malaria

Abstract

Malaria detection is very time consuming process, and its efficiency is impacted by the type of hardware, software used and the experience of pathologists. Deep learning has superior results in a wide range of applications, especially in data analysis. Therefore, this study gives an extensive review of malaria analysis using deep learning. To detect the images of the malaria parasite, researchers have proposed several models, such as ResNet, VGG-16, etc., to extract features. Compared to other deep learning techniques the CNN methods offer higher effectiveness and efficiency for malaria datasets for better diagnosis decisions. The main goal of this paper is to present the most popular pre trained CNN models used for analysis of microscopic malaria images and investigate the performance of each model.