Research Article |
Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors
Author(s): Mohammad Anwarul Siddique1*, Shailendra Kumar Singh2 and Moin Hasan3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 11, Issue 2, Special Issue on Mobile Computing assisted by Artificial Intelligent for 5G/6G Radio Communication
Publisher : FOREX Publication
Published : 30 June 2023
e-ISSN : 2347-470X
Page(s) : 590-595
Abstract
Visually cancer is the abnormal pattern with predefined structure could be found in liver Computed Tomography (CT) images. Using deep convolution neural network computation and image processing, this detected abnormal pattern cluster can be classified in different liver issue types. Full size liver CT scan images consisting different body parts, and these are ultrasonic based gray scaled image construction. The primary challenge in the cancer symptoms detection process is to extract the liver area out of image then finding out the actual area of abnormality to conclude whether abnormality is cancer or any other issues on liver. This is two stage processes, first is to segment the abnormality area and second is to perform pattern matching to identify the abnormality. This research paper primarily focuses on different pre-processing techniques and stages involved in liver abnormality segmentation
Keywords: Region of Interest
, Deep Learning
, Image Segmentation
, Machine Learning
, Edge Detection
, modality
, DICOM
.
Mohammad Anwarul Siddique*, Research Scholar, School of computer Science and Engineering, Lovely Professional university, phagwara, India; Email: Anwarul.42000323@lpu.in
Shailendra Kumar Singh, Assistant Professor, School of computer Science and Engineering, Lovely Professional university, phagwara, India; Email: drsksingh.cse@gmail.com
Moin Hasan, Assistant Professor, Department of computer Science and Engineering, Jahangirabad Institute of Technolog, Barabanki, India; Email: mmoinhhasan@gmail.com
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