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Abd Elaziz M, Dahou A, Aseeri AO, Ewees AA, Al-Qaness MAA, Ibrahim RA. Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment. Comput Biol Chem 2024; 111:108110. [PMID: 38815500 DOI: 10.1016/j.compbiolchem.2024.108110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/19/2024] [Accepted: 05/19/2024] [Indexed: 06/01/2024]
Abstract
The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; MEU Research Unit, Middle East University, Amman 11831, Jordan.
| | - Abdelghani Dahou
- Mathematics and Computer Science Department, University of Ahmed DRAIA, 01000, Adrar, Algeria; LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, 01000, Adrar, Algeria.
| | - Ahmad O Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt.
| | - Mohammed A A Al-Qaness
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China; Zhejiang Optoelectronics Research Institute, Jinhua 321004, China; College of Engineering and Information Technology, Emirates International University, Sana'a 16881, Yemen.
| | - Rehab Ali Ibrahim
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.
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Yaqoob A, Verma NK, Aziz RM. Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm. J Med Syst 2024; 48:10. [PMID: 38193948 DOI: 10.1007/s10916-023-02031-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024]
Abstract
Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data.
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Affiliation(s)
- Abrar Yaqoob
- School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466114, India.
| | - Navneet Kumar Verma
- School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466114, India
| | - Rabia Musheer Aziz
- School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466114, India
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Günal Ö, Akpinar M. Solving the laminar boundary layer problem in heat transfer with heuristic optimization techniques. Heliyon 2023; 9:e16955. [PMID: 37484250 PMCID: PMC10361041 DOI: 10.1016/j.heliyon.2023.e16955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 07/25/2023] Open
Abstract
Heat transfer takes place in every aspect of our daily life. Many situations, such as energy conversion plants, heating devices, and cooling systems, focus on heat transfer. One of the subjects in heat transfer is the boundary layer of the laminar flow problem. Well-known exploratory algorithms are used to solve for the flow on a flat plate in this study. The algorithms used are genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization for continuous domains (ACOR), artificial bee colony (ABC), and firefly algorithm (FA). The three properties, the layer thickness of the laminar boundary, heat flux, and the distance of the leading edge, are optimized. Each property is determined in three conditions; minimum, maximum, and target. The results showed that PSO, SA, ABC, and FA algorithms were more suitable than GA and ACOR algorithms. It has also been determined that the processing times are long in the FA and SA algorithms. The findings show that heuristic algorithms can find global results or results close to global results in heat transfer problems.
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Affiliation(s)
- Özen Günal
- Department of Computer Programming, Manisa Celal Bayar University, Manisa, Turkey
| | - Mustafa Akpinar
- Computer Information Science, Higher Colleges of Technology, Sharjah, United Arab Emirates
- Department of Software Engineering, Sakarya University, Sakarya, Turkey
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Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-47. [PMID: 37359740 PMCID: PMC10220350 DOI: 10.1007/s11831-023-09928-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
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Affiliation(s)
- Mohammad H. Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006 Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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Reenadevi R, Sathiyabhama B, Sankar S, Pandey D. Breast cancer detection in digital mammography using a novel hybrid approach of Salp Swarm and Cuckoo Search algorithm with deep belief network classifier. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2161149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- R. Reenadevi
- Department of Computer Science and Engineering, Sona College of Technology, Salem, India
| | - B. Sathiyabhama
- Department of Computer Science and Engineering, Sona College of Technology, Salem, India
| | - S. Sankar
- Department of Computer Science and Engineering, Sona College of Technology, Salem, India
| | - Digvijay Pandey
- Department of Technical Education, IET, Dr A.P.J Abdul Kalam Technical University, Lucknow, India
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Multistrategy Improved Sparrow Search Algorithm Optimized Deep Neural Network for Esophageal Cancer. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1036913. [PMID: 36203733 PMCID: PMC9532078 DOI: 10.1155/2022/1036913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 01/09/2023]
Abstract
Deep neural network is a complex pattern recognition network system. It is widely favored by scholars for its strong nonlinear fitting ability. However, training deep neural network models on small datasets typically realizes worse performance than shallow neural network. In this study, a strategy to improve the sparrow search algorithm based on the iterative map, iterative perturbation, and Gaussian mutation is developed. This optimized strategy improved the sparrow search algorithm validated by fourteen benchmark functions, and the algorithm has the best search accuracy and the fastest convergence speed. An algorithm based on the iterative map, iterative perturbation, and Gaussian mutation improved sparrow search algorithm is designed to optimize deep neural networks. The modified sparrow algorithm is exploited to search for the optimal connection weights of deep neural network. This algorithm is implemented for the esophageal cancer dataset along with the other six algorithms. The proposed model is able to achieve 0.92 under all the eight scoring criteria, which is better than the performance of the other six algorithms. Therefore, an optimized deep neural network based on an improved sparrow search algorithm with iterative map, iterative perturbation, and Gaussian mutation is an effective approach to predict the survival rate of esophageal cancer.
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PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5667264. [PMID: 35602611 PMCID: PMC9117073 DOI: 10.1155/2022/5667264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/29/2022] [Indexed: 02/06/2023]
Abstract
Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process's 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.
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A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105138] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Determining the risk priorities for the building stock in highly seismic-prone regions and making the final decisions about the buildings is one of the essential precautionary measures that needs to be taken before the earthquake. This study aims to develop an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock. For this purpose, the network parameters in the network structure have been optimized by establishing a hybrid structure with the Genetic Algorithm (GA). As a result, the ANN model can make accurate predictions with maximum efficiency. The suggested ANN model is a feedforward back-propagation network model. It aims to predict the risk priorities for 329 RC buildings in the most successful way, for which the performance score was calculated using the Turkey Rapid Evaluation Method (2013). In this paper, a GA-ANN hybrid model was implemented in which the ANN, using the most successful gene revealed by the model, produced successful results in calculating the performance score. In addition, the required input parameters for obtaining more efficient results in solving such a problem and the parameters that need to be used in establishing such an ANN network structure have been optimized. With the help of such a model, the operation process will be eliminated. The created hybrid model was 98% successful in determining the risk priority in RC buildings.
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Radiomic Features Based Severity Prediction in Dementia MR Images Using Hybrid SSA-PSO Optimizer and Multi-class SVM Classifier. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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10
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Kirankaya C, Aykut LG. Training of artificial neural networks with the multi-population based artifical bee colony algorithm. NETWORK (BRISTOL, ENGLAND) 2022; 33:124-142. [PMID: 35445626 DOI: 10.1080/0954898x.2022.2062472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/25/2022] [Accepted: 03/31/2022] [Indexed: 06/14/2023]
Abstract
Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is directly related to how the network is trained. The traditional algorithms, which are used to train artificial intelligence networks, do not always yield successful results in complicated problems and real-life problems. Metaheuristic algorithms are efficient techniques developed in order to figure out time-consuming and challenging problems fast and as optimally as possible. This study makes use of the artificial bee colony algorithm, which has been widely used recently in solving many kinds of problems so as to train artificial neural networks efficiently. Within this study, different strategies are used on subpopulations, so that the algorithm can search without getting tangled with the local optima. And also same and different parameter settings are considered for each population to reflect different search behaviours. The proposed approach was analysed through applied results of different data sets. The results yielded that the used strategies can be promising alternatives to the current search algorithms.
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Affiliation(s)
- Cihat Kirankaya
- Industrial Engineering, Graduate School of Natural and Applied Science, Erciyes University, Kayseri, Turkey
| | - Latife Gorkemli Aykut
- Industrial Engineering, Faculty of Engineering, Department of Industrial Engineering, Erciyes University, Kayseri, Turkey
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Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images. Comput Biol Med 2021; 139:105011. [PMID: 34753080 DOI: 10.1016/j.compbiomed.2021.105011] [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: 07/02/2021] [Revised: 10/12/2021] [Accepted: 10/31/2021] [Indexed: 11/21/2022]
Abstract
Analysing local texture and generating features are two key issues for automatic cancer detection in mammographic images. Recent researches have shown that deep neural networks provide a promising alternative to hand-driven features which suffer from curse of dimensionality and low accuracy rates. However, large and balanced training data are foremost requirements for deep learning-based models and these data are not always available publicly. In this work, we propose a fully-automated method for breast cancer diagnosis that performs training using small sets of data. Feature extraction from mammographic images is performed using a genetic-programming-based descriptor that exploits statistics on a local binary pattern-like local distribution defined in each pixel. The effectiveness of the suggested method is demonstrated on two challenging datasets, (1) the digital database for screening mammography and (2) the mammographic image analysis society digital mammogram database, for content-based image retrieval as well as for abnormality/malignancy classification. The experimental results show that the proposed method outperforms or achieves comparable results with deep learning-based methods even those with transfer learning and/or data-augmentation.
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Bülbül MA, Öztürk C. Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-06168-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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