Hasan Ali M, Jalab Hamid A, Ibrahim Rabha W, Meziane Farid, Al-Shamasneh Ala'a R, Obaiys Suzan J
College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq.
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
Entropy (Basel). 2020 Sep 15;22(9):1033. doi: 10.3390/e22091033.
Brain tumor detection at early stages can increase the chances of the patient's recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP-DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
早期检测脑肿瘤可以增加患者治疗后康复的机会。在过去十年中，我们注意到医学成像技术有了显著发展，它们现在正成为诊断和治疗过程中不可或缺的一部分。在本研究中，我们通过使用量子演算对以马尔萨利亚公式定义的熵差概念（通常用于描述两个不同的图形、雕像等）进行了推广。然后我们利用该结果扩展局部二值模式（LBP）以得到量子熵局部二值模式（QELBP）。所提出的研究包括两种MRI脑部扫描特征提取方法，即QELBP和深度学习DL特征。通过利用MRI脑部扫描中QELBP-DL特征提取对脑部的优异性能，改进了MRI脑部扫描的分类。将所有提取的特征相结合，在将长短期记忆网络用作脑肿瘤分类器时提高了其分类准确率。对于一个包含154个MRI脑部扫描的数据集进行分类所达到的最大准确率为98.80%。实验结果表明，组合提取的特征可提高MRI脑肿瘤分类的性能。
