الفهرس 
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Abstract
Accurately diagnosing the disease is one of the doctors’ concerns so that they can determine the appropriate action to combat this disease. For the visual portrayal of the functionality of tissues and organs, medical imaging has become a baseline in medical intervention and diagnosis (Müller & Kramer, 2021). Therefore, researchers are interested in effective methods that perform the process of classification and segmentation of medical images which are of major significance in disease diagnosis and analysis. However, researchers cannot spontaneously analyze medical images with computers or obtain image recognition clearly without functional medical image segmentation approaches (Feng-Ping & Zhi-Wen, 2019).The goal of semantic segmentation is learning and then classifying each pixel in an image with a class (e.g., background or tumor) to detect common characteristics in a medical input image. The process of cell segmentation is significant and difficult. It involves dividing a microscopic image area into segments that each illustrate a single instance of a cell. It is recognized as a basis of cellular study using images and is a major step in many scientific studies. A properly segmented image can record morphological data that is physiologically significant. It is too hard to manually segment cell instances from large cell datasets, so it is necessary to use automatic analysis techniques for medical images. Although there are several methods have already been created in this field (Xing et al., 2018), there is still a need to study datasets with low contrast ratios, large cell densities, and inadequate edge information.