Taylor political monarch butterfly optimization driven deep learning model for acute lymphoblastic leukemia detection and severity classification using blood smear images
Gnana Prakasam Mercy Bai, Venkadesh Perumal · Concurrency and Computation Practice and Experience · 2022
Summary An image processing and classification become an exciting research region for indicative probe because of the expansion of digital microscopic imaging. Acute lymphoblastic leukemia (ALL) is caused owing to broken in bone marrow, which is general among the people around the biosphere. A leukemia generally affects blood creating organs and hence, easily spread to other tissues by blood. The hematologists afflict more to differentiate leukemia existence in body of patients based on blood smears. Hence, in this article, Taylor political monarch butterfly optimization‐enabled deep residual neural network (Taylor PMBO‐enabled DRN) is devised for ALL classification. Segmentation is the process of separating the images into small pixels. A deep joint segmentation model effectively enhances the detection accuracy and it has better segmentation capability. For a better classification procedure, the important features, such as statistical, texture, and grid features, are extracted. For ALL detection and classification processes, a deep residual neural network (DRN) classifier is trained by the Taylor PMBO. The devised Taylor PMBO‐based DRN model obtained better performance than other method by testing accuracy, sensitivity, specificity, F1‐score, and precision of 0.9656, 0.9666, 0.9709, 0.9280, and 0.9321.