Brain tumour classification using MRI images based on lenet with golden teacher learning optimization.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2024-02-01 Epub Date: 2024-02-08 DOI:10.1080/0954898X.2023.2275720

Srilakshmi Aluri, Sagar S Imambi

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引用次数: 0

Abstract

Brain tumour (BT) is a dangerous neurological disorder produced by abnormal cell growth within the skull or brain. Nowadays, the death rate of people with BT is linearly growing. The finding of tumours at an early stage is crucial for giving treatment to patients, which improves the survival rate of patients. Hence, the BT classification (BTC) is done in this research using magnetic resonance imaging (MRI) images. In this research, the input MRI image is pre-processed using a non-local means (NLM) filter that denoises the input image. For attaining the effective classified result, the tumour area from the MRI image is segmented by the SegNet model. Furthermore, the BTC is accomplished by the LeNet model whose weight is optimized by the Golden Teacher Learning Optimization Algorithm (GTLO) such that the classified output produced by the LeNet model is Gliomas, Meningiomas, and Pituitary tumours. The experimental outcome displays that the GTLO-LeNet achieved an Accuracy of 0.896, Negative Predictive value (NPV) of 0.907, Positive Predictive value (PPV) of 0.821, True Negative Rate (TNR) of 0.880, and True Positive Rate (TPR) of 0.888.

查看原文本刊更多论文

使用基于lenet的MRI图像对脑瘤进行分类，并进行黄金教师学习优化。

脑瘤（BT）是一种危险的神经系统疾病，由颅骨或大脑中的异常细胞生长引起。如今，BT患者的死亡率呈线性增长。早期发现肿瘤对于患者的治疗至关重要，这可以提高患者的生存率。因此，本研究使用磁共振成像（MRI）图像进行BT分类（BTC）。在本研究中，使用非局部均值（NLM）滤波器对输入MRI图像进行预处理，该滤波器对输入图像进行去噪。为了获得有效的分类结果，通过SegNet模型对MRI图像中的肿瘤区域进行分割。此外，BTC由LeNet模型完成，LeNet模型的权重由Golden Teacher学习优化算法（GTLO）优化，使得LeNet模型产生的分类输出是胶质瘤、脑膜瘤和垂体瘤。实验结果表明，GTLO LeNet的准确度为0.896，负预测值（NPV）为0.907，正预测值（PPV）为0.821，真阴性率（TNR）为0.880，真阳性率（TPR）为0.8 88。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.