[This article belongs to Volume - 54, Issue - 03]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-01-05-2022-174

Title : Detected Sickle Cell Disorder using Deep Learning and fuzzy logic
Amen Adnan Khabeer,

Abstract : Nowadays, the most rapid challenge lies in developing an Artificial Intelligent(AI) system that manipulates disorders and choric diseases with high accuracy and efficiency. The disorders, which are related to blood groups (white and red blood cells), have attention to tackle these days. in consequence, unformed and faded boundaries of Red Blood Cells(RBCs) in the case of sickle cell anemia disorder affect people's lives, and, they have required a high level of AI diagnostic system through allocation and detection during the segmentation process. The proposed paper suggests a hybrid algorithm to detect and define Sickle Cell Disorder(SCD) depending on data preprocessing before passing the image to the detector system. The preprocessing data involves removing unwanted boundaries via applying a fuzzy logic filter to enhance the appearance of input then facilities the segmentation and regression process. After that, YOLO v5, the well-known detector in this ear, will operate the process of abject allocation. The result of this paper shows the high efficiency of dictation for SCD and segregation it from other red blood cells.