Machine Learning based Analysis and Detection of Malaria Disease

By Samir Kumar Bandyopadhyay

Malaria has been a well-researched domain over the last 2 decades. While clinical research pertaining to curing of Malaria has been majorly conclusive, detection of malaria parasite in digitized thin blood smear image still stands open for investigation. Manual investigation of malaria parasite in thin blood smear image stands tedious and is liable to be affected by human bias. Herein lies the need for an automated CAD system. The proposed system is aimed to serve as a trustworthy aid to pathologists in malaria parasite detection and subsequent stage/specie interpretation. The system has two broad phases, namely, the initial screening phase and main phase.

• Format: Paperback
• Language: English
• ISBN: 6202524308
• ISBN13: 9786202524308
• Release Date: April 2020
• Publisher: LAP Lambert Academic Publishing
• Length: 172 Pages
• Weight: 0.57 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"