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Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence
Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network
In the fast-growing field of medicine and its dynamic demand in research, a study that proves significant improvement to healthcare seems imperative especially when it is on cancer research. This research paved way to such significant findings by the inclusion of feature selection as one of its major components. Feature selection has become a vital task to apply data mining algorithms effectively in the real-world problems for classification. Feature selection has been the focus of interest for quite some time and much completed work related to it. Although much research conducted on the field, a study that proved a nearly perfect accuracy seems limited; hence, more scientifically driven results should be produced. Using various research on feature selection as basis for the choices in this study, the method was product of careful selection and planning. Specifically, this study used feature selection for improving classification accuracy on cancerous dataset. This study proposed Artificial Neural Network (ANN) for cancer classification with feature selection on colon cancer dataset. The study used best first search method in weka tools for feature selection. Through the process, a promising result has been achieved. The result of the experiment achieved 98.4 % accuracy for cancer classification after feature selection by using proposed algorithm. The result displayed that feature selection improved the classification accuracy based on the experiment conducted on the colon cancer dataset. The result of this experiment was comparable with the other studies on colon cancer research. It  showed another significant improvement and can be considered promising for more future applications
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review
The challenge of finding a global optimum in a solution search space with
limited resources and higher accuracy has given rise to several optimization
algorithms. Generally, the gradient-based optimizers converge to the global
solution very accurately, but they often require a large number of iterations
to find the solution. Researchers took inspiration from different natural
phenomena and behaviours of many living organisms to develop algorithms that
can solve optimization problems much quicker with high accuracy. These
algorithms are called nature-inspired meta-heuristic optimization algorithms.
These can be used for denoising signals, updating weights in a deep neural
network, and many other cases. In the state-of-the-art, there are no systematic
reviews available that have discussed the applications of nature-inspired
algorithms on biomedical signal processing. The paper solves that gap by
discussing the applications of such algorithms in biomedical signal processing
and also provides an updated survey of the application of these algorithms in
biomedical image processing. The paper reviews 28 latest peer-reviewed relevant
articles and 26 nature-inspired algorithms and segregates them into thoroughly
explored, lesser explored and unexplored categories intending to help readers
understand the reliability and exploration stage of each of these algorithms
Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool
ORIGINAL ARTICLES Epidemiology Biostatistics and Public Health - 2020, Volume 17, Number 2Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA toolInvestigation of diagnostic value of artificialintelligence systems in the diagnosis of breastcancer based on histopathological imagesusing Meta-MUMS DTA toolABSTRACTBackground: Various artificial intelligence systems are available for diagnosing breast cancer based onhistopathological images. Assessing the performance of existing methodologies for breast cancer diagnosis is vital.Methods: The SCOPUS database has been searched for studies up to December 15, 2018. We extracted the data,including "true positive," "true negative," "false positive," and "false negative". The pooled sensitivity, pooled specificity,positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve of summary receiveroperating characteristic curve were useful in assessing the diagnostic accuracy. Egger's test, Deeks' funnel plot, SVE(Smoothed Variance regression model based on Egger’s test), SVT (Smoothed Variance regression model based onThompson’s method), and trim and fill methodologies were essential tests for publication bias identification.Results: Three studies with eight approaches from thirty-seven articles were found eligible for further analysis. Asensitivity of 0.95, a specificity of 0.78, a PLR of 7525, an NLR of 0.06, a DOR of 88.15, and an AUC of 0.953showed high significant heterogeneity; however, the reason was not the threshold effect. The publication bias wasdetected by SVE, SVT, and trim and fill analysis.Conclusion: The artificial intelligent (AI) systems play a pivotal role in the diagnosis of breast cancer usinghistopathological cell images and are important decision-makers for pathologists. The analyses revealed that theoverall accuracy of AI systems is promising for breast cancer; however, the pooled specificity is lower than pooledsensitivity. Moreover, the approval of the results awaits conducting randomized clinical trials with sufficient dat
Improved Reptile Search Optimization Algorithm using Chaotic map and Simulated Annealing for Feature Selection in Medical Filed
The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets
Computational models and approaches for lung cancer diagnosis
The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results
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