Identification and Classification for Diagnosis of Malaria Disease using Blood Cell Images
Hafiz Muhammad Bilal · Lahore Garrison University Research Journal of Computer Science and Information Technology · 2023
Machine Learning is a subfield of artificial intelligence that focuses on developing intelligentalgorithms capable of learning from available data without requiring constant programming, enablingthem to adapt to different environments based on current scenarios. These algorithms are crucial inmaking intelligent decisions and conducting thorough analyses to uncover intricate patternsconcealed within the data. This study used multiple machine-learning classification algorithms toanalyze patients' data based explicitly on input images containing parasite-infected and uninfectedMalaria samples. AI techniques were utilised to measure the presence of parasites in the images. Theimage classification system was designed to accurately identify malaria parasites in blood images bygenerating image features related to color, texture, and cell and parasite geometry. A classifier basedon SVM (Support Vector Machine) provided by Weka was employed to differentiate betweenparasite-infected and non-infected blood images. Through extensive experimentation, it wasdetermined that SVM strategies exhibited significant relevance, achieving a cross-validation accuracyof 99.4% in the basic diagnosis of malaria fever. This finding holds great potential in assistingclinicians with accurate infection diagnoses.