efficient and low complex architecture for detection and classification of brain tumor using rcnn with two channel cnn论文.pdf

Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx
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Journal of King Saud University –
Computer and Information Sciences
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Efficient and low complex architecture for detection and classification
of Brain Tumor using RCNN with Two Channel CNN
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Nivea Kesav , . Jibukumar
Division of Electronics and Communication, School of Engineering, Cochin University of Science & Technology, Kochi 682022, India
article info abstract
Article history: The Brain Tumor is one of the most serious scenarios associated with the brain where a cluster of abnor-
Received 18 March 2021 mal cells grows in an uncontrolled fashion. The field of image processing has experienced remarkable
Revised 7 May 2021 growth in the area of biomedical applications with the invention of different techniques in deep learning.
Accepted 18 May 2021
Brain tumor classification and detection is a subject of prime importance where Convolutional Neural
Available online xxxx
Networks (CNN) find application. But the main drawback of the existing technology is that it is complex
with a huge number of parameters contributing to high execution time and high system specifications for
Keywords:
implementation. In this paper, a novel architecture for Brain tumor classification and tumor type object
Deep learning
detection using the RCNN technique is prop