Optimization on malaria computer aided diagnostic system
In the field, malaria infection are analyzed by manually examining a thin blood smear that is acquired from the infected patient. This method requires a trained human interaction and therefore it is time consuming and prone to errors. A development of Computer Aided Diagnostics (CAD) for Malaria were conducted before. The result of the program is able to recognize the infected red blood cells, the species and the life phase of the infecting Plasmodium. However, the average runtime of the program is 41.45 seconds per image, which is too long if the program is to be used in the field. By measuring the runtime of each process in the program, optimization can be done by re-writing or substituting the algorithm that causes the longest runtime. The optimization to the Connected Component Labelling process by substituting it with Contour Tracing Labelling caused the program to be 8 times faster. By reducing the resolution of the image before the Clump Splitting process results in speedup by 14 times faster. Applying Concave Point Based Clump Splitting speeds up the runtime by 23 times faster compared to the original CAD. Optimization is successfully performed so that the CAD has an average runtime of 1.73 seconds while only affecting accuracy by a small margin, from 63% to 74% on the Infected vs Healthy Classification, 66% to 71% on the Species Classification, and from 73% to 67% on the Life Phase Classification.
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