Adaptive blood cell segmentation and hybrid Learning-based blood cell classification: A Meta-heuristic-based model

The fundamental goal of this paper is to develop a novel blood cell classification using a hybrid learning model. The proposed model encompasses different processing steps like "(a) pre-processing, (b) cell segmentation, (c) Feature extraction, and (d) classification ". In the initial step, the blood smear images are pre-processed using Red Green Blue (RGB) scale to gray scale conversion and contrast enhancement. Then, the Adaptive Fuzzy C-Means (A-FCM) clustering with heuristic improvement is developed for blood cell classification. During testing, the feature extraction from the segmented cell image is performed by the Gray Level Co-occurrence Matrix1 (GLCM), Local Binary Pattern (LBP), geometric features, and color features. These features are subjected to the hybrid learning model with Neural Network (NN) and Long Short-Term Memory (LSTM) termed NLSTM. The modification of the A-FCM-based cell segmentation and hybrid learning-based cell classification is performed by a Best search-based Moth-Flame Optimization (BS-MFO) algorithm. The experimental analysis specifies that the suggested model has shown better efficiency on the identification of blood cell images, and attains high accuracy when compared over the competitive methods.