This paper aims at investigating the best feature selection method for optimized and automated machine learning based detection of malarial parasite in wholeslide images of peripheral blood smears. We do this by extracting samples from the wholeslide images and performing feature extraction. A host of feature selection methods are used to judge the performance of the Support Vector Machine as a binary classifier. For each feature selection method, the Support Vector Machine is trained using the significant features. We perform cross-validation and grid-search for finding the best SVM parameters. The trained SVM is subsequently used to classify known instances of "Normal" and "Infected" samples that are taken randomly from the wholeslide image but unknown to the SVM. Confusion matrices are generated for each classification performed. Various performance measures of each classification task are reported. We conclude that based on our experiments, the binary SVM classifier yields a superlative accuracy of 95.5% if the feature-selection is based on Kullback-Leibler distance between the two classes.
['Vishnu Muralidharan', 'Yuhang Dong', 'W. David Pan']
A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images