Critical Analysis of Malaria Parasite Detection Using Machine Learning Technique

Malaria is caused by the protozoan parasites of the genus Plasmodium and is transmitted through the bite of a female Anopheles mosquito. The disease is most often curable if there is early detection and proper diagnosis of the disease. This paper is to identify presence of malarial parasite in thin blood smear digitized images. The images are obtained using digital microscope and were analysed using a computer aided system. A common, portable imaging standards, Digital Imaging and Communications in Medicine (DICOM) was introduced to help the seamless integration of image analysis, image transmission and storage across different platforms including cloud storage. Similarly, Picture Archiving and Communication System (PACS) for common viewing of images across different imaging modalities. The method is tested in State Hospital at Kolkata, India, over 1000 patients with symptoms of malaria and in most of the cases the diagnosis correctly identified malaria parasite present in the patients. The accuracy for malaria parasite detection by the proposed system was recorded as 98.11% (Sensitivity-0.9645, Specificity-1, AUC-0.9583).