1.1. The need for an automated malaria diagnosis process

The issues associated with manual diagnosis present the case for automation of the malaria diagnosis process. The automation of the diagnosis process will ensure accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Hence, rural areas suffering from lack of specialized infrastructure and trained manpower can benefit greatly from automated diagnosis. Automating the diagnosis of malaria involves adapting the methods, expertise, practices, and knowledge of conventional microscopy to a computerized system structure [3]. Early detection of malaria is essential for ensuring proper diagnosis and increasing chances of cure. In consideration of the severity and the number of fatalities claimed by this disease, it is rational to accept potential small implementation errors introduced by an automated system. An automated system consists of streamlined image processing techniques for initial filtering and segmentation and suite of pattern recognition and/or machine learning algorithms directed toward robustly recognizing infected cells in a light or wholeslide microscopic image [4]. Previous studies have shown that the degree of agreement between clinicians on the severity of the disease in a given patent's sample is very low. Hence, a computer-assisted system as a decision support system can be paramount to faster and reliable diagnosis. It can help provide a benchmark and standardized way of measuring the degree of infection of the disease [5].
