MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Abstract

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.

A Study on Certain Subclasses of Analytic Functions Involving the Jackson q-Difference Operator

“…Otherwise, by Lemma 1, there exists a point z 0 = qz such that z 0 D q ω(z 0 ) = kω(z 0 ), (11) where k ≥ 1. ( 10) in conjunction with (11) yields…”

“…Following the same idea, the q-difference operator has been extensively investigated in the field of GFT by many authors. For some recent works related to this operator on the classes of analytic functions, we refer to [8][9][10][11][12][13][14][15]. The theory of q-series is based on the observation that…”

Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction

“…Recently, deep learning algorithms have established a notable presence in computer vision and image analysis 16,17 . Various studies have utilized deep learning algorithms for the detection and classification of breast cancer.…”

“…The LSTM is a recurrent neural network architecture which was originally proposed by Hochreiter and Schmidhuber to overcome the limitations of artificial neural networks and deal with the vanishing gradient problem that comes from a small vanishing of the gradient which effectively prevent the weight from changing its value. Resulting in stopping completely the neural network from further training 17 . It is capable of successfully learning data with long-term temporal dependencies, especially in sequence prediction problems due to the significant time lag between the input and its corresponding output.…”

Quantum Neurobiology

“…Whereas qubits are a relatively simple system, expanding to higher dimension qudits (quantum information digits) is non-trivial as it is difficult to quantify the quantum correlations in the system (using the diagonalization of correlation matrices for bipartite systems) [65]. Other quantum MRI tumor segmentation projects use a quantum entropy classification method [66] and a quantum filtering technique (for noise reduction preprocessing) together with a quantum artificial immune system-inspired SoftMax function in a deep spiking neural network (SNN) architecture [67]. Quantum algorithms are also deployed to analyze CT scans; for example, to classify quantum data comparing COVID-19 and non-COVID-19 patient influenza and virus pneumonia lung CT scans, analyzed with TensorFlow Quantum and a D-Wave Systems quantum annealer [68].…”