Ahmed Heba M, Youssef Bayumy A B, Elkorany Ahmed S, Saleeb Adel A, Abd El-Samie Fathi
Appl Opt. 2018 Mar 1;57(7):B25-B31. doi: 10.1364/AO.57.000B25.
Automated and accurate classification of magnetic resonance images (MRIs) of the brain has great importance for medical analysis and interpretation. This paper presents a hybrid optimized classification method to classify the brain tumor by classifying the given magnetic resonance brain image as normal or abnormal. The proposed system implements a gray wolf optimizer (GWO) combined with a supervised artificial neural network (ANN) classifier to achieve enhanced MRI classification accuracy via selecting the optimal parameters of ANN. The introduced GWO-ANN classification system performance is compared to the traditional neural network (NN) classifier using receiver operating characteristic analysis. Experimental results obviously indicate that the presented system achieves a high classification rate and performs much better than the traditional NN classifier.
大脑磁共振成像（MRI）的自动准确分类对医学分析和解读具有重要意义。本文提出了一种混合优化分类方法，通过将给定的大脑磁共振图像分类为正常或异常来对脑肿瘤进行分类。所提出的系统实现了一种灰狼优化器（GWO）与监督式人工神经网络（ANN）分类器相结合的方法，通过选择ANN的最优参数来提高MRI分类准确率。使用接收器操作特征分析将引入的GWO-ANN分类系统性能与传统神经网络（NN）分类器进行比较。实验结果明显表明，所提出的系统实现了高分类率，并且比传统NN分类器表现得更好。
