Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm

Manic, K. Suresh; Biju, Roshima; Patel, Warish; Khan, Muhammad Attique; Raja, N. Sri Madhava; Uma, S.

doi:10.1155/2021/5524637

Cited by 19 publications

(13 citation statements)

References 62 publications

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“…In image processing, the input images are refined by a few filters (i.e., Gaussian filter and Weiner filter) and followed by segmentation of the object [ 15 , 16 ]. The output of this step is utilized for feature extraction (i.e., texture, color, and point), which are classified using the ML algorithms like support vector machine (SVM) and to name a few more [ 17 , 18 ]. This domain's development, especially deep learning, has shown great success in segmentation and classification tasks [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Syed

Khan

Tariq

et al. 2021

Behavioural Neurology

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The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).

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Section: Introductionmentioning

confidence: 99%

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Syed

Khan

Tariq

et al. 2021

Behavioural Neurology

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“…Moreover, multiple aspects including readability, correctness, completeness, and compactness of documents can be considered to improve the quality of summary. Moreover, the deep learning models will be considered for the data extraction and optimized using metaheuristic techniques [56][57][58][59][60][61][62].…”

Section: Discussionmentioning

confidence: 99%

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

et al. 2021

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Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

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“…Moreover, in the researches of diseased tissue in some brain areas, the brain was usually used as the research objects. If brain tissue image was to be segmented, the scalp, skull, and other nontissue components must be removed first, which would greatly reduce the effect of nonbrain tissue components on the segmentation [ 15 , 16 ]. Then, the brain tissue image was further divided into gray matter and white matter, and the obtained results were more conducive to subsequent quantitative analysis.…”

Section: Methodsmentioning

confidence: 99%

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

Liu

Zhang

Niu

et al. 2022

Contrast Media & Molecular Imaging

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This study was aimed to explore the diagnostic value of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in brain tumors under the fuzzy C-means (FCM) algorithm. The two-dimensional FCM hybrid algorithm was improved to be three-dimensional. The MRI images and MRS spectra of 127 patients with brain tumors (low-grade glioma group) and 54 healthy people (healthy group) were analyzed. The results suggested that the membership matrix of the improved algorithm had lower ambiguity, higher segmentation accuracy, closer relationship of intrapixels, and stronger irrelevance of interclass pixels. Through the analysis of gray matter volume, it was found that, compared with the healthy group, the gray matter and white matter volumes in the brain of high-grade glioma were higher, and those of low-grade glioma group were lower. The improved FCM algorithm could obtain a higher accuracy of 88.64% in segmenting images. It had a higher sensitivity to gray matter changes in brain tumors, reaching 92.72%; its specificity was not much different from that of traditional FCM, which were 83.61% and 88.06%, respectively. In the diagnostic value, the area under the curve of mean kurtosis was the largest, which was 0.962 ( P < 0.001). The best critical value was 0.4096, which had a greater reference significance for clinical treatment and prognosis. The ratio of choline/N-acetyl-aspartate and the ratio of choline/creatine also showed significant differences in high- and low-grade gliomas ( P < 0.05), but the specificity and sensitivity were slightly lower. It also had guiding significance for the grading of gliomas. Overall, the improved FCM algorithm had obvious advantages in the segmentation process of MRI images, which provided help for the clinical diagnosis of brain tumors.

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Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm

Cited by 19 publications

References 62 publications

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

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