Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptive<scp>neuro‐fuzzy</scp>inference system‐particle swarm optimization approach for breast tumour classification

Tzalavra, Alexia G.; Andreadis, Ioannis; Dalakleidi, Kalliopi; Constantinidis, Fotios; Zacharaki, Evangelia I.; Nikita, Konstantina S.

doi:10.1111/exsy.12895

Cited by 7 publications

(2 citation statements)

References 81 publications

(105 reference statements)

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“…We have considered the classifier with the best results as the best performer. Mubarak et al [ 9 ] provide instant access to a variety of classification techniques, such as KNN, SVM, LDA [ 39 ], LR [ 40 ], DT [ 41 ], and NB [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

Prince,

Niu,

Khan

et al. 2024

BMC Bioinformatics

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Background COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time. Results In this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient. Conclusion Our proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

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Section: Methodsmentioning

confidence: 99%

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

Prince,

Niu,

Khan

et al. 2024

BMC Bioinformatics

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“…A more advanced approach is utilizing Curvelet Transform (CT) as a feature extraction method for its ability to obtain both linear and curved edges along multiple scales and orientations [42]. In this regard, several studies have applied CT in various computer vision tasks, namely tumor detection [43], [44], image segmentation [45], [46], [47], signature verification [48], [49], and face recognition [50], [51], [52]. However, despite its advantages, limited number of studies have reported using CT as a feature extraction tool for AD detection using MRI images [53], [54].…”

Section: Introductionmentioning

confidence: 99%

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

2023

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Alzheimer's Disease (AD) is the most common form of dementia. It usually manifests through progressive loss of cognitive function and memory, subsequently impairing the person's ability to live without assistance and causing a tremendous impact on the affected individuals and society. Currently, AD diagnosis relies on cognitive tests, blood tests, behavior assessments, brain imaging, and medical history analysis. However, these procedures are subjective and inconsistent, making an accurate prediction for the early stages of AD difficult. This paper introduces a curvelet transform (CT) based-convolutional neural network (CNN) (DeepCurvMRI) model for improving the accuracy of early-stage AD disease detection using from Magnetic resonance imaging (MRI) images. The MRI images were first pre-processed using CT, and then a CNN model was trained using the new image representation. In this study, we used Alzheimer's MRI images dataset hosted on the Kaggle platform to train DeepCurvMRI for multi and binary classification tasks. DeepCurvMRI achieved an accuracy, sensitivity, specificity, and F1 score of 98.62% ± 0.10%, 99.05% ± 0.10%, 98.50% ± 0.03%, and 99.21 ± 0.08, respectively, using the leave-one-group-out (LOGO) cross-validation approach in multi-classification task. The highest accuracy obtained in binary classification is 98.71% ± 0.05%. In addition to LOGO, DeepCurvMRI was tested using randomly stratified 10-fold and 5-fold cross validation. These encouraging results are superior to the ones reported in related methods, showcasing the potentiality of DeepCurvMRI in capturing the key anatomical changes in MRI images that can be differentiated between various staged of Alzheimer's disease classes.INDEX TERMS Alzheimer's disease, curvelet transform, deep learning, CNN, MRI images.

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Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

Hamdan

Wulamu

Zhang

et al. 2023

Int J Syst Assur Eng Manag

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Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

Cited by 7 publications

References 81 publications

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

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