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. 2022 Jun 29;10(7):1218.
doi: 10.3390/healthcare10071218.

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

M Ramamoorthy  1 , Shamimul Qamar  2 , Ramachandran Manikandan  3 , Noor Zaman Jhanjhi  4 , Mehedi Masud  5 , Mohammed A AlZain  6
Affiliations

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

M Ramamoorthy et al. Healthcare (Basel). .

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

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed architecture of AHCN-LNQ.
Figure 2
Figure 2
Operation of input image1.
Figure 3
Figure 3
Operation of input image2.
Figure 4
Figure 4
Operation of input image3.
Figure 5
Figure 5
Comparison of accuracy.
Figure 6
Figure 6
Comparison of precision.
Figure 7
Figure 7
Comparison of specificity.
Figure 8
Figure 8
Accuracy of training sets.
Figure 9
Figure 9
Loss function of training set.
Figure 10
Figure 10
Loss function of testing set.
See this image and copyright information in PMC

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