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Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network
SIMULATION ( IF 1.6 ) Pub Date : 2020-08-21 , DOI: 10.1177/0037549720948595
Li Xu 1 , Qi Gao 1 , Nasser Yousefi 2
Affiliation  

Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

中文翻译:

基于离散小波变换、灰度共生矩阵和最优深度信念网络的脑肿瘤诊断

脑肿瘤是一组起源于中枢神经系统不同细胞或大脑其他组织癌症的癌症。大脑中过度生长的细胞被称为肿瘤。肿瘤细胞需要食物和血液才能生存。颅腔中肿瘤细胞的生长和增殖会导致大脑内部紧张,从而破坏重要的人体结构。因此,在脑肿瘤的早期阶段进行诊断至关重要。本研究为脑肿瘤的早期诊断引入了一种新的优化方法。该方法主要有降噪、肿瘤分割、形态学、基于小波和灰度共生矩阵的特征提取、基于优化深度置信网络的分类五个主要部分。为了优化分类器网络,使用了蛾搜索算法的增强版本。将仿真结果应用于三个不同的数据集 FLAIR、T1 和 T2,并将所提出方法的准确性结果与其他两种元启发式算法、粒子群优化和 Bat 算法进行了比较。最终结果表明,所提出的技术对比较方法具有良好的成就。
更新日期:2020-08-21
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