A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.</p

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 — sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics

Image multi-level-thresholding with Mayfly optimization

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization(BFO), firefly-algorithm(FA), bat algorithm (BA), cuckoo search(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this wor

Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

Detection of organs in CT images using Neural Networks

Táto práca sa zaoberá výskumom zobrazovacích metód v medicíne, klasických prístupov k segmentácii obrázkov, CT a konvolučným neuronovým sietiam. Praktickou časťou je implementácia architektúry 3D UNet pre segmentáciu chrbtice a jednotlivých stavcov z CT obrázkov a jej porovnanie s jej 2D verziou.This thesis contains research of the field of medical imaging, classical methods of image segmentation, computed tomography and convolutional neural networks. The practical part involves implementation of an architecture of 3D UNet for segmentation of the spine and specific vertebrae from CT scans. Furthermore, this architecture is compared to its 2D counterpart

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

Identification of continuous-time model of hammerstein system using modified multi-verse optimizer

his thesis implements a novel nature-inspired metaheuristic optimization algorithm, namely the modified Multi-Verse Optimizer (mMVO) algorithm, to identify the continuous-time model of Hammerstein system. Multi-Verse Optimizer (MVO) is one of the most recent robust nature-inspired metaheuristic algorithm. It has been successfully implemented and used in various areas such as machine learning applications, engineering applications, network applications, parameter control, and other similar applications to solve optimization problems. However, such metaheuristics had some limitations, such as local optima problem, low searching capability and imbalance between exploration and exploitation. By considering these limitations, two modifications were made upon the conventional MVO in our proposed mMVO algorithm. Our first modification was an average design parameter updating mechanism to solve the local optima issue of the traditional MVO. The essential feature of the average design parameter updating mechanism is that it helps any trapped design parameter jump out from the local optima region and continue a new search track. The second modification is the hybridization of MVO with the Sine Cosine Algorithm (SCA) to improve the low searching capability of the conventional MVO. Hybridization aims to combine MVO and SCA algorithms advantages and minimize the disadvantages, such as low searching capability and imbalance between exploration and exploitation. In particular, the search capacity of the MVO algorithm has been improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA) that will be able to balance the processes of exploration and exploitation. The mMVO based method is then used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. Note that the structure of the linear and nonlinear subsystems is assumed to be known. Moreover, a continuous-time linear subsystem is considered in this study, while there are a few methods that utilize such models. Two numerical examples and one real-world application, such as the Twin Rotor System (TRS) are used to illustrate the efficiency of the mMVO-based method. Various nonlinear subsystems such as quadratic and hyperbolic functions (sine and tangent) are used in those experiments. Numerical and experimental results are analyzed to focus on the convergence curve of the fitness function, the parameter variation index, frequency and time domain response and the Wilcoxon rank test. For the numerical identifications, three different levels of white noise variances were taken. The statistical analysis value (mean) was taken from the parameter deviation index to see how much our proposed algorithm has improved. For Example 1, the improvements are 29%, 33.15% and 36.68%, and for the noise variances, 0.01, 0.25, and 1.0 improvements can be found. For Example 2, the improvements are 39.36%, 39.61% and 66.18%, and for noise variances, the improvements are by 0.01, 0.25 and 1.0, respectively. Finally, for the real TRS application, the improvement is 7%. The numerical and experimental results also showed that both Hammerstein model subsystems are defined effectively using the mMVO-based method, particularly in quadratic output estimation error and a differentiation parameter index. The results further confirmed that the proposed mMVObased method provided better solutions than other optimization techniques, such as PSO, GWO, ALO, MVO and SCA

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users’ awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Automatic Bone Structure Segmentation of Under-Sampled CT/FLT-PET Volumes for HSCT Patients

In this thesis I present a pipeline for the instance segmentation of vertebral bodies from joint CT/FLT-PET image volumes that have been purposefully under-sampled along the axial direction to limit radiation exposure to vulnerable HSCT patients. The under-sampled image data makes the segmentation of individual vertebral bodies a challenging task, as the boundaries between the vertebrae in the thoracic and cervical spine regions are not well resolved in the CT modality, escaping detection by both humans and algorithms. I train a multi-view, multi-class U-Net to perform semantic segmentation of the vertebral body, sternum, and pelvis object classes. These bone structures contain marrow cavities that, when viewed in the FLT-PET modality, allow us to investigate hematopoietic cellular proliferation in HSCT patients non-invasively. The proposed convnet model achieves a Dice score of 0.9245 for the vertebral body object class and shows qualitatively similar performance on the pelvis and sternum object classes. The final instance segmentation is realized by combining the initial vertebral body semantic segmentation with the associated FLT-PET image data, where the vertebral boundaries become well-resolved by the 28th day post-transplant. The vertebral boundary detection algorithm is a hand-crafted spatial filter that enforces vertebra span as an anatomical prior, and it performs similar to a human for the detection of all but one vertebral boundary in the entirety of the HSCT patient dataset. In addition to the segmentation model, I propose, design, and test a “drop-in” replacement up-sampling module that allows state-of-the-art super-resolution convnets to be used for purely asymmetric upscaling tasks (tasks where only one image dimension is scaled while the other is held to unity). While the asymmetric SR convnet I develop falls short of the initial goal, where it was to be used to enhance the unresolved vertebral boundaries of the under-sampled CT image data, it does objectively upscale medical image data more accurately than naïve interpolation methods and may be useful as a pre-processing step for other medical imaging tasks involving anisotropic pixels or voxels