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Rai HM, Yoo J, Razaque A. A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction. Med Biol Eng Comput 2024:10.1007/s11517-024-03158-0. [PMID: 39012415 DOI: 10.1007/s11517-024-03158-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/22/2024] [Indexed: 07/17/2024]
Abstract
The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.
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Affiliation(s)
- Hari Mohan Rai
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-Gu, Seongnam-Si, 13120, Gyeonggi-Do, Republic of Korea.
| | - Joon Yoo
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-Gu, Seongnam-Si, 13120, Gyeonggi-Do, Republic of Korea
| | - Abdul Razaque
- Department of Cyber Security, Information Processing and Storage, Satbayev University, Almaty, Kazakhstan
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Rai HM, Yoo J, Atif Moqurrab S, Dashkevych S. Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. MEASUREMENT 2024; 225:114059. [DOI: 10.1016/j.measurement.2023.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Kermouni Serradj N, Messadi M, Lazzouni S. Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach. J Digit Imaging 2022; 35:1544-1559. [PMID: 35854037 DOI: 10.1007/s10278-022-00677-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 04/12/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022] Open
Abstract
Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature.
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Affiliation(s)
- Nadia Kermouni Serradj
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria.
| | - Mahammed Messadi
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria
| | - Sihem Lazzouni
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria
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Self-adaptive salp swarm algorithm for optimization problems. Soft comput 2022. [DOI: 10.1007/s00500-022-07280-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Breast Cancer Semantic Segmentation for Accurate Breast Cancer Detection with an Ensemble Deep Neural Network. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10856-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Rajendran R, Balasubramaniam S, Ravi V, Sennan S. Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier. Comput Intell 2022. [DOI: 10.1111/coin.12522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Reenadevi Rajendran
- Department of Computer Science and Engineering Sona College of Technology Salem India
| | | | - Vinayakumar Ravi
- Centre for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia
| | - Sankar Sennan
- Department of Computer Science and Engineering Sona College of Technology Salem India
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TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073273] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.
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Lin F, Sun H, Han L, Li J, Bao N, Li H, Chen J, Zhou S, Yu T. An effective fine grading method of BI-RADS classification in mammography. Int J Comput Assist Radiol Surg 2021; 17:239-247. [PMID: 34940931 DOI: 10.1007/s11548-021-02541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 11/30/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Mammography is an important imaging technique for the detection of early breast cancer. Doctors classify mammograms as Breast Imaging Reporting and Data Systems (BI-RADS). This study aims to provide an intelligent BI-RADS grading prediction method, which can help radiologists and clinicians to distinguish the most challenging 4A, 4B, and 4C cases in mammography. METHODS Firstly, the breast region, the lesion region, and the corresponding region in the contralateral breast were extracted. Four categories of features were extracted from the original images and the images after the wavelet transform. Secondly, an optimized sequential forward floating selection (SFFS) was used for feature selection. Finally, a two-layer classifier integration was employed for fine grading prediction. 45 cases from the hospital and 500 cases from Digital Database for Screening Mammography (DDSM) database were used for evaluation. RESULTS The classification performance of the support vector machine (SVM), Bayes, and random forest is very close on the 45 testing set, with the area under the receiver operating characteristic curve (AUC) of 0.978, 0.967, and 0.968. On the DDSM set, the AUC achieves 0.931, 0.938, and 0.874. Using the mean probability prediction, the AUC on the two datasets reaches 0.998 and 0.916. However, they are all significantly higher than the doctors' diagnosis, with the AUC of 0.807 and 0.725. CONCLUSIONS A BI-RADS fine grading (2, 3, 4A, 4B, 4C, 5) prediction model was proposed. Through the evaluation from different datasets, the performance is proved higher than that of the doctors, which may provide great help for clinical BI-RADS classification diagnosis. Therefore, our method can produce more effective and reliable results.
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Affiliation(s)
- Fei Lin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Hang Sun
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Lu Han
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Jing Li
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Nan Bao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Hong Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Jing Chen
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Shi Zhou
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China.
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Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112412122] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.
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Xia J, Yang D, Zhou H, Chen Y, Zhang H, Liu T, Heidari AA, Chen H, Pan Z. Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Comput Biol Med 2021; 141:105137. [PMID: 34953358 DOI: 10.1016/j.compbiomed.2021.105137] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 11/16/2022]
Abstract
Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions. Meanwhile, DFSCA is integrated into KELM to establish a new machine learning model named DFSCA-KELM. Firstly, using the CEC2017 benchmark suite, the exploration and exploitation capabilities of DFSCA were demonstrated. Secondly, evaluation of the model DFSCA-KELM on six medical datasets extracted from the UCI machine learning repository for medical diagnosis proved the effectiveness of the proposed model. At last, the model DFSCA-KELM was applied to solve two real medical cases, and the results indicate that DFSCA-KELM can also deal with practical medical problems effectively. Taken together, these results show that the proposed technique can be regarded as a promising tool for medical diagnosis.
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Affiliation(s)
- Jianfu Xia
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China; Soochow University, Soochow, Jiangsu, 215000, China.
| | - Daqing Yang
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Hong Zhou
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Yuyan Chen
- Department of Anorectal Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Hongliang Zhang
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Tong Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Zhifang Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
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Mohanty F, Dora C. An optimized KELM approach for the diagnosis of COVID-19 from 2D-SSA reconstructed CXR Images. OPTIK 2021; 244:167572. [PMID: 34248209 PMCID: PMC8260491 DOI: 10.1016/j.ijleo.2021.167572] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 06/30/2021] [Indexed: 05/17/2023]
Abstract
The COVID-19 is proved to be the most infectious disease of the current century with a high mortality rate world-wide. The current RT-PCR test standard for the diagnosis of COVID-19 is an invasive and time-consuming procedure, whereas the chest X-ray (CXR) images provide a non-invasive and time/cost-effective method for COVID-19 diagnosis. The current existing deep learning methods for the detection and diagnosis of CXR images provide biased results for the small size dataset available. Hence, in the present work, a conventional yet efficient method is proposed classifying the CXR images into COVID-19, Pneumonia, and Normal. The proposed approach pre-processes the CXR images using 2D singular spectrum analysis (SSA) for image reconstruction which enhances the feature inputs to the classifier. The features are extracted from the reconstructed images using a block-based GLCM approach. Then, a grasshopper-based Kernel extreme learning machine (KELM) is proposed which finds the optimal features and kernel parameters of KELM at the same instance. From the experimental analysis, it is seen that the present work outperforms that of other competent schemes in terms of classification accuracy with a minimal set of features extracted from the first 2 eigen components of the 2D-SSA reconstructed image with 5 × 5 decomposition.
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Affiliation(s)
- Figlu Mohanty
- School of Engineering & Technology, Centurion University of Technology & Management, Odisha, India
| | - Chinmayee Dora
- School of Engineering & Technology, Centurion University of Technology & Management, Odisha, India
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Multi-criterion decision making-based multi-channel hierarchical fusion of digital breast tomosynthesis and digital mammography for breast mass discrimination. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Dabass J, Hanmandlu M, Vig R. Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-021-04616-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
AbstractWith aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.
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Zhang Y, Liu R, Heidari AA, Wang X, Chen Y, Wang M, Chen H. Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.038] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Ceylan O. Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05062-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Altay O, Ulas M, Alyamac KE. DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC. PeerJ Comput Sci 2021; 7:e411. [PMID: 33817052 PMCID: PMC7959629 DOI: 10.7717/peerj-cs.411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/03/2021] [Indexed: 05/13/2023]
Abstract
Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.
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Altan A, Karasu S. Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. CHAOS, SOLITONS, AND FRACTALS 2020; 140:110071. [PMID: 32834627 PMCID: PMC7332960 DOI: 10.1016/j.chaos.2020.110071] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 06/23/2020] [Accepted: 07/01/2020] [Indexed: 05/14/2023]
Abstract
The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei Province, China in late December 2019, is rapidly spreading and affecting all countries in the world. Real-time reverse transcription-polymerase chain reaction (RT-PCR) test has been described by the World Health Organization (WHO) as the standard test method for the diagnosis of the disease. However, considering that the results of this test are obtained between a few hours and two days, it is very important to apply another diagnostic method as an alternative to this test. The fact that RT-PCR test kits are limited in number, the test results are obtained in a long time, and the high probability of healthcare personnel becoming infected with the disease during the test, necessitates the use of other diagnostic methods as an alternative to these test kits. In this study, a hybrid model consisting of two-dimensional (2D) curvelet transformation, chaotic salp swarm algorithm (CSSA) and deep learning technique is developed in order to determine the patient infected with coronavirus pneumonia from X-ray images. In the proposed model, 2D Curvelet transformation is applied to the images obtained from the patient's chest X-ray radiographs and a feature matrix is formed using the obtained coefficients. The coefficients in the feature matrix are optimized with the help of the CSSA and COVID-19 disease is diagnosed by the EfficientNet-B0 model, which is one of the deep learning methods. Experimental results show that the proposed hybrid model can diagnose COVID-19 disease with high accuracy from chest X-ray images.
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Affiliation(s)
- Aytaç Altan
- Department of Electrical Electronics Engineering, Zonguldak Bülent Ecevit University, 67100 Zonguldak, Turkey
| | - Seçkin Karasu
- Department of Electrical Electronics Engineering, Zonguldak Bülent Ecevit University, 67100 Zonguldak, Turkey
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Shaikh TA, Ali R. An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Classification of digital mammograms using information set features and Hanman Transform based classifiers. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100401] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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