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Jakkaladiki SP, Maly F. Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis. PeerJ Comput Sci 2024; 10:e1850. [PMID: 38435578 PMCID: PMC10909230 DOI: 10.7717/peerj-cs.1850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 01/10/2024] [Indexed: 03/05/2024]
Abstract
Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.
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Affiliation(s)
- Sudha Prathyusha Jakkaladiki
- Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
| | - Filip Maly
- Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
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Akbarzadeh MR, Ghafourian H, Anvari A, Pourhanasa R, Nehdi ML. Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16114200. [PMID: 37297334 DOI: 10.3390/ma16114200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Abstract
Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.
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Affiliation(s)
| | - Hossein Ghafourian
- Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Arsalan Anvari
- Department of Construction Engineering and Management, Faculty of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
| | - Ramin Pourhanasa
- Department of Civil Engineering, College of Engineering, Shahrekord University, Shahrekord 64165478, Iran
| | - Moncef L Nehdi
- Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
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Huang Q, Ding H, Rashid Sheykhahmad F. A skin cancer diagnosis system for dermoscopy images according to deep training and metaheuristics. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Mehmood K, Chaudhary NI, Khan ZA, Cheema KM, Raja MAZ. Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model. Biomimetics (Basel) 2023; 8:biomimetics8020141. [PMID: 37092393 PMCID: PMC10123741 DOI: 10.3390/biomimetics8020141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 04/03/2023] Open
Abstract
In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.
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Affiliation(s)
- Khizer Mehmood
- Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan
| | - Naveed Ishtiaq Chaudhary
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou 64002, Taiwan
- Correspondence:
| | - Zeshan Aslam Khan
- Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan
| | - Khalid Mehmood Cheema
- Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi 46000, Pakistan
| | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou 64002, Taiwan
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Jiang Y, Zhang D, Zhu W, Wang L. Multi-Level Thresholding Image Segmentation Based on Improved Slime Mould Algorithm and Symmetric Cross-Entropy. ENTROPY (BASEL, SWITZERLAND) 2023; 25:178. [PMID: 36673319 PMCID: PMC9858507 DOI: 10.3390/e25010178] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Multi-level thresholding image segmentation divides an image into multiple regions of interest and is a key step in image processing and image analysis. Aiming toward the problems of the low segmentation accuracy and slow convergence speed of traditional multi-level threshold image segmentation methods, in this paper, we present multi-level thresholding image segmentation based on an improved slime mould algorithm (ISMA) and symmetric cross-entropy for global optimization and image segmentation tasks. First, elite opposition-based learning (EOBL) was used to improve the quality and diversity of the initial population and accelerate the convergence speed. The adaptive probability threshold was used to adjust the selection probability of the slime mould to enhance the ability of the algorithm to jump out of the local optimum. The historical leader strategy, which selects the optimal historical information as the leader for the position update, was found to improve the convergence accuracy. Subsequently, 14 benchmark functions were used to evaluate the performance of ISMA, comparing it with other well-known algorithms in terms of the optimization accuracy, convergence speed, and significant differences. Subsequently, we tested the segmentation quality of the method proposed in this paper on eight grayscale images and compared it with other image segmentation criteria and well-known algorithms. The experimental metrics include the average fitness (mean), standard deviation (std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), which we utilized to evaluate the quality of the segmentation. The experimental results demonstrated that the improved slime mould algorithm is superior to the other compared algorithms, and multi-level thresholding image segmentation based on the improved slime mould algorithm and symmetric cross-entropy can be effectively applied to the task of multi-level threshold image segmentation.
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Affiliation(s)
- Yuanyuan Jiang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China
- Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China
| | - Dong Zhang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China
| | - Wenchang Zhu
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China
| | - Li Wang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China
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Ma BJ, Pereira JLJ, Oliva D, Liu S, Kuo YH. Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2022.110247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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7
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Ibrahim AM, Tawhid MA. Chaotic electromagnetic field optimization. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10324-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: comparative analysis, open challenges and new trends. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04064-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Deng Q, Shi Z, Ou C. Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution. ENTROPY 2022; 24:e24030319. [PMID: 35327830 PMCID: PMC8947459 DOI: 10.3390/e24030319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 02/04/2023]
Abstract
In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.
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A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:3694723. [PMID: 34447429 PMCID: PMC8384530 DOI: 10.1155/2021/3694723] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 07/24/2021] [Accepted: 08/04/2021] [Indexed: 01/02/2023]
Abstract
Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed for early detection of this cancer. Here, we design a new convolutional neural network for this purpose. Marine predators algorithm is also used for optimal arrangement and better network accuracy. The method finally applied to RIDER dataset, and the results are compared with some pretrained deep networks, including CNN ResNet-18, GoogLeNet, AlexNet, and VGG-19. Final results showed higher results of the proposed method toward the compared techniques. The results showed that the proposed MPA-based method with 93.4% accuracy, 98.4% sensitivity, and 97.1% specificity provides the highest efficiency with the least error (1.6) toward the other state of the art methods.
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Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106510] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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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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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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Sparavigna AC. Entropy in Image Analysis. ENTROPY 2019; 21:e21050502. [PMID: 33267216 PMCID: PMC7514991 DOI: 10.3390/e21050502] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 12/15/2022]
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