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

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

Dynamic behavior of impinging drops on water repellent surfaces: Machine learning-assisted approach to predict maximum spreading

The study of drop dynamic undergoing collision with solid surfaces seems quite necessary due to its practical applications ranging from coating industries to anti-icing and self-cleaning surfaces. Therefore, we experimentally studied the dynamic of impinging drop on water-repellent surfaces for a wide range of drop properties and initial velocities in terms of weber number (We). We considered the maximum spreading diameter to quantify the spreading dynamic. We modified one of the existing energy-balance models to analytically predict the observed maximum spreading diameters. We showed that above a critical We number (roughly 60–80), the maximum spreading diameter of superhydrophobic surfaces starts to deviate from those of hydrophobic surfaces. Therefore, we incorporated an adjusting factor into the energy-balance model to consider the transition from hydrophobicity to superhydrophobicity. Moreover, we developed a machine learning approach to predict the maximum spreading diameter as a function of drop properties and surface characteristics. Using the machine learning approach, it was found that beyond a critical contact angle (CAadv ∼ 150°–160°) the maximum spreading diameter does not depend on the contact angle anymore. Moreover, for low We numbers, the maximum spreading diameter decrease with increasing the contact angle, while for high We numbers they are directly proportional