An improved Henry gas solubility optimisation-based feature selection approach for histological image taxonomy
by E. Susheela Vishnoi; Ajit Kumar Jain
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 20, No. 1, 2021

Abstract: Classification of histopathological images is one of the important areas of research in the field of medical imaging. However, the complexities available in histopathological images make the classification process difficult. For such complex images, selection of prominent features for image classification is also a challenging task and is still an open research area for computer vision researchers. Therefore, an effective method for the selection of prominent features of images has been introduced in this work. For the same, an improved Henry gas solubility optimisation has been introduced in which a new position update equation has been used to balance the global and local search. The selected features are then input to classifiers to identify histopathological images. For the performance analysis of improved Henry gas solubility optimisation, 23 benchmark functions are used. The proposed feature selection method has been analysed over two datasets, namely breast cancer cell dataset and ICIAR grand challenge dataset. The proposed feature selection method eliminates the maximum 60% average features from both the datasets. To validate usefulness of selected features, results of different classifiers are compared. Experimental results show that the presented method outperforms other methods.

Online publication date: Thu, 29-Apr-2021

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