Earlier Detection of Brain Tumor by Pre-Processing Based on Histogram Equalization with Neural Network

Abstract

MRI is an influential diagnostic imaging technology specifically worn to detect pathological changes in tissues with organs early. It is also a non-invasive imaging method. Medical image segmentation is a complex and challenging process due to the intrinsic nature of images. The most consequential imaging analytical approach is MRI, which has been in use to detect abnormalities in tissues and human organs. The portrait was actualized for CAD (computer-assisted diagnosis) utilizing image processing techniques with deep learning, initially to perceive a brain tumor in a person with early signs of brain tumor. Using AHCN-LNQ (adaptive histogram contrast normalization with learning-based neural quantization), the first image is preprocessed. When compared to extant techniques, the simulation outcome shows that this proposed method achieves an accuracy of 93%, precision of 92%, and 94% of specificity.

Keywords

• brain tumor
• ML (machine learning)
• identification of brain tumor
• CAD (computer-aided diagnosis)
• (AHCN-LNQ) adaptive histogram contrast normalization with learning-based neural quantization