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Athisayamani S, Antonyswamy RS, Sarveshwaran V, Almeshari M, Alzamil Y, Ravi V. Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification. Diagnostics (Basel) 2023; 13:diagnostics13040668. [PMID: 36832156 PMCID: PMC9955169 DOI: 10.3390/diagnostics13040668] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%.
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Affiliation(s)
| | - Robert Singh Antonyswamy
- Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India
| | - Velliangiri Sarveshwaran
- Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India
| | - Meshari Almeshari
- Department of Diagnostic Radiology, College of Applied Medical Sciences, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Yasser Alzamil
- Department of Diagnostic Radiology, College of Applied Medical Sciences, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
- Correspondence:
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A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Artif Intell Rev 2023; 56:1627-1698. [PMID: 35693000 PMCID: PMC9170564 DOI: 10.1007/s10462-022-10209-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.
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Qiao S, Yu Q, Zhao Z, Song L, Tao H, Zhang T, Zhao C. Edge extraction method for medical images based on improved local binary pattern combined with edge-aware filtering. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Tavakoli-Zaniani M, Sedighi-Maman Z, Fazel Zarandi MH. Segmentation of white matter, grey matter and cerebrospinal fluid from brain MR images using a modified FCM based on double estimation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Mishra AR, Rani P, Saha A. Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station. INT J INTELL SYST 2021. [DOI: 10.1002/int.22523] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Arunodaya Raj Mishra
- Department of Mathematics Government College Jaitwara Satna Madhya Pradesh India
| | - Pratibha Rani
- Department of Mathematics National Institute of Technology Warangal Telangana India
| | - Abhijit Saha
- Department of Mathematics Techno College of Engineering Agartala Agartala Tripura India
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Sparse Coding for Brain Tumor Segmentation Based on the Non-Linear Features. JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING 2021. [DOI: 10.4028/www.scientific.net/jbbbe.49.63] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The main aim of brain Magnetic Resonance Image (MRI) segmentation is to extractthe significant objects like tumors for better diagnosis and proper treatment. As the brain tumors are distinct in the sense of shapes, location, and intensity it is hard to define a general algorithm for the tumor segmentation. Accurate extraction of tumors from the brain MRIs is a challenging task due to the complex anatomical structure of brain tissues in addition to the existence of intensity inhomogeneity, partial volume effects, and noise. In this paper, a method of Sparse coding based on non-linear features is proposed for the tumor segmentation from MR images of the brain. Initially, first and second-order statistical eigenvectors of 3 × 3 size are extracted from the MRIs then the process of Sparse coding is applied to them. The kernel dictionary learning algorithm is employed to obtain the non-linear features from these processed vectors to build two individual adaptive dictionaries for healthy and pathological tissues. This work proposes dictionary learning based kernel clustering algorithm to code the pixels, and then the target pixelsare classified by utilizing the method of linear discrimination. The proposed technique is applied to several tumor MRIs, taken from the BRATS database. This technique overcomes the problem of linear inseparability produced by the high level intensity similarity between the normal and abnormal tissues of the given brain image. All the performance parameters are high for the proposed technique. Comparison of the results with some other existing methods such as Fuzzy – C- Means (FCM), Hybrid k-Mean Graph Cut (HKMGC) and Neutrosophic Set – Expert Maximum Fuzzy Sure Entropy (NS-EMFSE) demonstrates that the proposed sparse coding method is effective in segmenting the brain tumor regions.
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Wady SH, Yousif RZ, Hasan HR. A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8125392. [PMID: 32733955 PMCID: PMC7369660 DOI: 10.1155/2020/8125392] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/10/2020] [Accepted: 06/08/2020] [Indexed: 12/28/2022]
Abstract
Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.
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Affiliation(s)
- Shakhawan H. Wady
- Applied Computer, College of Medicals and Applied Sciences, Charmo University, Chamchamal, Sulaimani, KRG, Iraq
- Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, KRG, Iraq
- Department of Information Technology, University College of Goizha, Sulaimani, KRG, Iraq
| | - Raghad Z. Yousif
- Department of Physics, College of Science, Salahaddin University, Erbil, KRG, Iraq
- Department of IT, College of Information Technology, Catholic University in Erbil, KRG, Iraq
| | - Harith R. Hasan
- Department of Computer Science, Kurdistan Technical Institute, Sulaimani, KRG, Iraq
- Computer Science Institute, Sulaimani Polytechnic University, Sulaimani, KRG, Iraq
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Naji Alwerfali HS, A A Al-Qaness M, Abd Elaziz M, Ewees AA, Oliva D, Lu S. Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E328. [PMID: 33286101 PMCID: PMC7516786 DOI: 10.3390/e22030328] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 03/05/2020] [Accepted: 03/10/2020] [Indexed: 11/17/2022]
Abstract
Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.
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Affiliation(s)
- Husein S Naji Alwerfali
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mohammed A A Al-Qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt
| | - Diego Oliva
- Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara C.P. 44100, Jalisco, Mexico
| | - Songfeng Lu
- Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China
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Yin B, Wang C, Abza F. New brain tumor classification method based on an improved version of whale optimization algorithm. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101728] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Elaziz MA, Ewees AA, Yousri D, Alwerfali HSN, Awad QA, Lu S, Al-Qaness MAA. An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:125306-125330. [PMID: 34192114 PMCID: PMC8043509 DOI: 10.1109/access.2020.3007928] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/04/2020] [Indexed: 05/04/2023]
Abstract
Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of MathematicsFaculty of ScienceZagazig UniversityZagazig44519Egypt
| | - Ahmed A Ewees
- Department of ComputerDamietta UniversityDamietta34511Egypt
| | - Dalia Yousri
- Electrical Engineering DepartmentFaculty of EngineeringFayoum UniversityFaiyum63514Egypt
| | - Husein S Naji Alwerfali
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China
| | - Qamar A Awad
- Department of MathematicsFaculty of ScienceZagazig UniversityZagazig44519Egypt
| | - Songfeng Lu
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China
- Hubei Engineering Research Center on Big Data Security, School of Cyber Science and EngineeringHuazhong university of Science and TechnologyWuhan430074China
| | - Mohammed A A Al-Qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityWuhan430079China
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Şişik F, Sert E. Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware. Med Hypotheses 2019; 136:109507. [PMID: 31812927 DOI: 10.1016/j.mehy.2019.109507] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/11/2019] [Accepted: 11/16/2019] [Indexed: 01/20/2023]
Abstract
Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI's are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate.
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Affiliation(s)
- Fatih Şişik
- Göksun Vocational School, Department of Computer Programming, Kahramanmaras Sutcu Imam University, K.Maras, Turkey
| | - Eser Sert
- Department of Computer Engineering, Engineering and Architecture Faculty, Kahramanmaras Sutcu Imam University, K.Maras, Turkey.
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Sert E, Özyurt F, Doğantekin A. A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network. Med Hypotheses 2019; 133:109413. [PMID: 31586812 DOI: 10.1016/j.mehy.2019.109413] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/24/2019] [Accepted: 09/26/2019] [Indexed: 10/25/2022]
Abstract
Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order to distinguish between benign and malignant brain tumors. Many systems attempting to define these tumors are based on tissue analysis methods. However, various factors such as the quality of an MRI device, noisy images and low image resolution may decrease the quality of MRI images. To eliminate these problems, super resolution approaches are preferred as a complementary source for brain tumor images. The proposed method benefits from single image super resolution (SISR) and maximum fuzzy entropy segmentation (MFES) for brain tumor segmentation on an MRI image. Later, pre-trained ResNet architecture, which is a convolutional neural network (CNN) architecture, and support vector machine (SVM) are used to perform feature extraction and classification, respectively. It was observed in experimental studies that SISR displayed a higher performance in terms of brain tumor segmentation. Similarly, it displayed a higher performance in terms of classifying brain tumor regions as well as benign and malignant brain tumors. As a result, the present study indicated that SISR yielded an accuracy rate of 95% in the diagnosis of segmented brain tumors, which exceeds brain tumor segmentation using MFES without SISR by 7.5%.
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Affiliation(s)
- Eser Sert
- Department of Computer Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey.
| | - Fatih Özyurt
- Department of Informatics, Firat University, Elazig, Turkey
| | - Akif Doğantekin
- Emek Hospital, Department of Internal Diseases, Gaziantep, Turkey
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