Taylor political monarch butterfly optimization driven deep learning model for acute lymphoblastic leukemia detection and severity classification using blood smear images

Abstract

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.

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

“…As a result of genetic changes and complex mechanisms that are not yet fully elucidated, stem cells can stop their growth prematurely or acquire the ability to replicate indefinitely and become resistant to the mechanisms of programmed cell death [4]. When this happens, the immature cells invade the marrow, blood, and sometimes the lymph nodes, spleen, and liver, forming leukemia [5]. One factor that characterizes the disease is the speed of its development: acute forms have a short or very short course and prevent cell maturation, while chronic forms have a slow evolution, in which the ability of marrow progenitors to mature.…”