Peripheral blood cell classification using modified local-information weighted fuzzy C-means clustering-based golden eagle optimization model.

This paper presents a novel medical image processing technique for analyzing different peripheral blood cells such as monocytes, lymphocytes, neutrophils, eosinophils, basophils, and macrophages. However, the existing systems suffered from low accuracy while classifying the different blood cell images and also consume higher processing power. The proposed model consists of two major steps such as segmentation and classification of peripheral blood cells. The modified local-information weighted intuitionistic Fuzzy C-means clustering (MLWIFCM)-based golden eagle optimization algorithm performs the nucleus segmentation. Finally, the peripheral blood cell classes such as Basophil, Lymphocyte, Neutrophil, Monocyte, and Eosinophil are effectively classified using hybrid-parameter RNN-based remora optimization algorithm. The MATLAB R2019b is used as the implementation platform. To analyze the performances of our proposed method, we have taken two datasets; they are BCCD and LISC datasets. Meanwhile, the classification performances were analyzed with the aid of different performance metrics such as mean accuracy, mean intersection over union, mean average precision, and mean BF score values. [ABSTRACT FROM AUTHOR]