A comprehensive study of brain tumour discrimination using phase combinations, feature rankings, and hybridised classifiers

Koyuncu, Hasan; Barstuğan, Mücahid; Özi̇ç, Muhammet Üsame

doi:10.1007/s11517-020-02273-y

Cited by 11 publications

(35 citation statements)

References 22 publications

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“…Briefly, in the hybrid method, the calculation of the fitness in the optimization approach (e.g., the third and seventh items in Table 2 ) involves the operation of NNs and the determination of MSE rates for each network used [27] , [28] .…”

Section: Methodsmentioning

confidence: 99%

“…1 by originating the flow of GM-CPSO in training part of the classifier. As declared before, every network (weight-bias vector) can be considered as a particle (position vector) in the optimization method [27] , [28] , [29] , [31] .…”

Section: Methodsmentioning

confidence: 99%

“…FOS are the mean , standard deviation , skewness , kurtosis , energy , and entropy , obtained from a histogram or acquired directly from an image. As one of the most popular and preferred radiomics, FOS features do not vary among studies [27] . However, the GLCM, GLRLM, and GLSZM features can differ from one study to another, depending on work preferences or the advice of the literature.…”

Section: Methodsmentioning

confidence: 99%

“…Koyuncu et al. [27] considered FOS features along with five feature ranking approaches ( Bhattacharyya , entropy , Roc , t-test , and Wilcoxon ) to classify high and low grade gliomas (brain tumors) defined in different MRI-phase combinations.…”

Section: Introductionmentioning

confidence: 99%

“…In yet another study, Koyuncu et al. [27] used GM-CPSO–NN as the classifier unit to design a task-affirmative framework for brain tumor classification. Two optimized classifiers were compared with GM-CPSO–NN to identify the classifier that was appropriate for the construction of the framework.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier

Koyuncu

Barstuğan

2021

Signal Processing: Image Communication

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In medical imaging procedures for the detection of coronavirus, apart from medical tests, approval of diagnosis has special significance. Imaging procedures are also useful for detecting the damage caused by COVID-19. Chest X-ray imaging is frequently used to diagnose COVID-19 and different pneumonias. This paper presents a task-specific framework to detect coronavirus in X-ray images. Binary classification of three different labels (healthy, bacterial pneumonia, and COVID-19) was performed on two differentiated data sets in which corona is stated as positive. First-order statistics, gray level co-occurrence matrix, gray level run length matrix, and gray level size zone matrix were analyzed to form fifteen sub-data sets and to ascertain the necessary radiomics. Two normalization methods are compared to make the data meaningful. Furthermore, five feature ranking approaches ( Bhattacharyya , entropy , Roc , t-test , and Wilcoxon ) are mentioned to provide necessary information to a state-of-the-art classifier based on Gauss-map-based chaotic particle swarm optimization and neural networks. The proposed framework was designed according to the analyses about radiomics, normalization approaches, and filter-based feature ranking methods. In experiments, seven metrics were evaluated to objectively determine the results: accuracy, area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, g-mean, precision, and f-measure. The proposed framework showed promising scores on two X-ray-based data sets, especially with the accuracy and area under the ROC curve rates exceeding 99% for the classification of coronavirus vs . others.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier

Koyuncu

Barstuğan

2021

Signal Processing: Image Communication

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An intelligent medical image classification system using few‐shot learning

Abbas

2022

Concurrency and Computation

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Summary Due to the wide range of diseases and imaging modalities, a retrieving system is a challenging task to access the corresponding clinical cases from a large medical repository on time. Several intelligent systems are developed to recognize medical images based on various machine learning algorithms such as deep learning and transfer learning methods that have outperformed compared to traditional methods. However, they are time‐consuming and required more labeled data for training the network models. To overcome these issues, an intelligence medical imaging recognition (IMR‐FSL) system is developed based on three phases of the few‐short learning (FSL) concept, which required few labeled examples to train the model and they are capable to generalize on huge and unseen medical datasets. To test and train the performance of the proposed IMR‐FSL system, many imaging modalities and multiclass datasets such as TCIA and Kvasir are utilized. Statistical analysis is also performed to measure the performance of the IMR‐FSL system. On average, the IMR‐FSL system achieved SE of 95%, SP of 96.5%, ACC of 97.5%, and AUC of 0.96 on both datasets. The performance of the IMR‐FSL system demonstrates the effectiveness of other state‐of‐the‐art systems.

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Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

et al. 2023

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A comprehensive study of brain tumour discrimination using phase combinations, feature rankings, and hybridised classifiers

Cited by 11 publications

References 22 publications

COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier

COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier

An intelligent medical image classification system using few‐shot learning

Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

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