Heuristic Scale to Estimate Premature Malaria Parasites: Scope in Microscopic Blood Smear Images

Abstract

Objective: Malaria is one of the epidemic diseases and early detection of malaria symptoms in the patients using the current manual procedures are skeptical, as the diagnosis patterns depends more on the experience of professionals. To overcome the challenges, in this paper we are proposing computer aided model to support in malaria detection at early stages using Microscopic Blood Smear Images analysis using machine learning. Methods/Statistical Analysis: There are many computer aided models that were proposed and adapted in the process of addressing the diagnosis models. Some models like machine learning, image processing, neural network based solutions etc are adapted, which reflects more insights into the process. However, the issue of gaps in accuracy still persists, and the proposed model of Heuristic Scale to Estimate Premature Malaria Parasites Scope (SEMPS) with multi stage processing of the microscopic images of blood smear is processed. Findings: The proposed model is compared with the benchmark models like SVM and Bayesian, the outcome in terms of efficiency of the model is imperative from the results. The proposed model has resulted in more effective and accurate detection of malaria symptoms in the test cases, and the result accuracy is higher than the other two benchmarking models of SVM and Bayesian techniques chosen for comparative analysis. Improvements: The computational complexity of the SEMPS is evinced as linear, where the majority of benchmarking models are found to be up-hard.