MALARIA DISEASE CLASSIFICATION BASED ON MORPHOLOGICAL OPERATIONS AND PCA ALGORITHM

The constant challenge of malaria as a public health issue still remains extremely prevalent in areas with little or no infrastructure or access to health practitioners. Conventional blood-smear analysis is considered to be the "gold standard" approach to detecting malaria, yet it is extremely complex and requires a high level of expert human resources and is prone to error. To overcome these limitations, in the current study a methodology for automated diagnosis of malaria from blood smear images using machine learning algorithms is proposed. The work uses a Kaggle dataset of 27,560 images, split equally into parasitized and uninfected images. Morphological operations - erosion, gradient, bottom hat - are used to improve image quality and highlight important cellular elements. Subsequently, feature extraction is done through Principal Component Analysis (PCA) and classification is done using shallow neural networks, support-vector machines, random forests and logistic regression. Among these algorithms, the shallow neural network had 100 percent accuracy, recall, precision, and F1-score. The results of this study show that morphological preprocessing in combination with Principle Component Analysis (PCA) and simple classifiers could enable the rapid and reliable diagnosis of malaria in resource constrained settings.