QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network

Abstract

Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods’ reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched-Filter Technique (QMFT). Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS-DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21%, average error-squared rate is 0.006, signal-to-noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15%. The improved tumor is 74.50%, and the entire tumor is 91.92%, which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.

Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning

“…Computer-aided diagnosis (CAD) tools have been recently used to study various features' impact and identify various diseases from the patient data [ 19 ]. Computationally efficient artificial neural networks (ANNs) [ 20 , 21 ] have been utilized to monitor the patients' health status and diagnose various diseases such as COVID-19 and mental health disorders [ 21 ] using smartphones and smartwatches.…”

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

“…Instead of a single number, we are presented with a three-dimensional list (one cube) in which the neurons in the CNN are organized in three dimensions. As a consequence, this cube's output is a three-dimensional matrix as well [60,61].…”

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

“…The findings show that optimizing dropout-based CNNs is worthwhile, owing to the ease with which appropriate dropout likelihood values can be found without setting new parameters empirically. Another method in image processing is the optimized quantum matched-filter technique [ 32 ], robust principal component analysis [ 33 ], and the generalized autoregressive conditional heteroscedasticity model [ 34 ]. Moreover, expression programming [ 35 , 36 , 37 ].…”