Ramamoorthy M, Qamar Shamimul, Manikandan Ramachandran, Jhanjhi Noor Zaman, Masud Mehedi, AlZain Mohammed A
Department of Artificial Intelligence and Machine Learning, Saveetha Institute of Medical and Technical Science, Saveetha School of Engineering, Chennai 600124, India.
Computer Science and Engineering, Faculty of Sciences & Managements, King Khalid University, Dhahran Al Janub, Abha 64351, Saudi Arabia.
Healthcare (Basel). 2022 Jun 29;10(7):1218. doi: 10.3390/healthcare10071218.
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.
磁共振成像（MRI）是一种有影响力的诊断成像技术，专门用于早期检测组织和器官中的病理变化。它也是一种非侵入性成像方法。由于图像的内在性质，医学图像分割是一个复杂且具有挑战性的过程。最重要的成像分析方法是MRI，它已被用于检测组织和人体器官中的异常情况。利用深度学习的图像处理技术实现了用于计算机辅助诊断（CAD）的图像，最初是为了在有脑肿瘤早期迹象的人中识别脑肿瘤。使用基于学习的神经量化的自适应直方图对比度归一化（AHCN-LNQ）对第一幅图像进行预处理。与现有技术相比，模拟结果表明，该方法的准确率达到93%，精确率达到92%，特异性达到94%。
