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Houssein EH, Emam MM, Alomoush W, Samee NA, Jamjoom MM, Zhong R, Dhal KG. An efficient improved parrot optimizer for bladder cancer classification. Comput Biol Med 2024; 181:109080. [PMID: 39213707 DOI: 10.1016/j.compbiomed.2024.109080] [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: 05/18/2024] [Revised: 08/22/2024] [Accepted: 08/24/2024] [Indexed: 09/04/2024]
Abstract
Bladder Cancer (BC) is a common disease that comes with a high risk of morbidity, death, and expense. Primary risk factors for BC include exposure to carcinogens in the workplace or the environment, particularly tobacco. There are several difficulties, such as the requirement for a qualified expert in BC classification. The Parrot Optimizer (PO), is an optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots, but the PO algorithm becomes stuck in sub-regions, has less accuracy, and a high error rate. So, an Improved variant of the PO (IPO) algorithm was developed using a combination of two strategies: (1) Mirror Reflection Learning (MRL) and (2) Bernoulli Maps (BMs). Both strategies improve optimization performance by avoiding local optimums and striking a compromise between convergence speed and solution diversity. The performance of the proposed IPO is evaluated against eight other competitor algorithms in terms of statistical convergence and other metrics according to Friedman's test and Bonferroni-Dunn test on the IEEE Congress on Evolutionary Computation conducted in 2022 (CEC 2022) test suite functions and nine BC datasets from official repositories. The IPO algorithm ranked number one in best fitness and is more optimal than the other eight MH algorithms for CEC 2022 functions. The proposed IPO algorithm was integrated with the Support Vector Machine (SVM) classifier termed (IPO-SVM) approach for bladder cancer classification purposes. Nine BC datasets were then used to confirm the effectiveness of the proposed IPO algorithm. The experiments show that the IPO-SVM approach outperforms eight recently proposed MH algorithms. Using the nine BC datasets, IPO-SVM achieved an Accuracy (ACC) of 84.11%, Sensitivity (SE) of 98.10%, Precision (PPV) of 95.59%, Specificity (SP) of 95.98%, and F-score (F1) of 94.15%. This demonstrates how the proposed IPO approach can help to classify BCs effectively. The open-source codes are available at https://www.mathworks.com/matlabcentral/fileexchange/169846-an-efficient-improved-parrot-optimizer.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Waleed Alomoush
- School of Computing, Skyline University College, Sharjah, P.O. Box 1797, United Arab Emirates.
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Mona M Jamjoom
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Rui Zhong
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India.
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2
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Elshamy R, Abu-Elnasr O, Elhoseny M, Elmougy S. Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer. Sci Rep 2024; 14:19534. [PMID: 39174564 PMCID: PMC11341685 DOI: 10.1038/s41598-024-69193-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 08/01/2024] [Indexed: 08/24/2024] Open
Abstract
Optimizers are the bottleneck of the training process of any Convolutionolution neural networks (CNN) model. One of the critical steps when work on CNN model is choosing the optimal optimizer to solve a specific problem. Recent challenge in nowadays researches is building new versions of traditional CNN optimizers that can work more efficient than the traditional optimizers. Therefore, this work proposes a novel enhanced version of Adagrad optimizer called SAdagrad that avoids the drawbacks of Adagrad optimizer in dealing with tuning the learning rate value for each step of the training process. In order to evaluate SAdagrad, this paper builds a CNN model that combines a fine- tuning technique and a weight decay technique together. It trains the proposed CNN model on Kather colorectal cancer histology dataset which is one of the most challenging datasets in recent researches of Diagnose of Colorectal Cancer (CRC). In fact, recently, there have been plenty of deep learning models achieving successful results with regard to CRC classification experiments. However, the enhancement of these models remains challenging. To train our proposed model, a learning transfer process, which is adopted from a pre-complicated defined model is applied to the proposed model and combined it with a regularization technique that helps in avoiding overfitting. The experimental results show that SAdagrad reaches a remarkable accuracy (98%), when compared with Adaptive momentum optimizer (Adam) and Adagrad optimizer. The experiments also reveal that the proposed model has a more stable training and testing processes, can reduce the overfitting problem in multiple epochs and can achieve a higher accuracy compared with previous researches on Diagnosis CRC using the same Kather colorectal cancer histology dataset.
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Affiliation(s)
- Reham Elshamy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
| | - Osama Abu-Elnasr
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
| | - Mohamed Elhoseny
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
| | - Samir Elmougy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
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3
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Shi Y, Tang S, Li Y, He Z, Tang S, Wang R, Zheng W, Chen Z, Zhou Y. Continual learning for seizure prediction via memory projection strategy. Comput Biol Med 2024; 181:109028. [PMID: 39173485 DOI: 10.1016/j.compbiomed.2024.109028] [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: 04/01/2024] [Revised: 06/30/2024] [Accepted: 08/12/2024] [Indexed: 08/24/2024]
Abstract
Despite extensive algorithms for epilepsy prediction via machine learning, most models are tailored for offline scenarios and cannot handle actual scenarios where data changes over time. Catastrophic forgetting(CF) for learned electroencephalogram(EEG) data occurs when EEG changes dynamically in the clinical setting. This paper implements a continual learning(CL) strategy Memory Projection(MP) for epilepsy prediction, which can be combined with other algorithms to avoid CF. Such a strategy enables the model to learn EEG data from each patient in dynamic subspaces with weak correlation layer by layer to minimize interference and promote knowledge transfer. Regularization Loss Reconstruction Algorithm and Matrix Dimensionality Reduction Algorithm are introduced into the core of MP. Experimental results show that MP exhibits excellent performance and low forgetting rates in sequential learning of seizure prediction. The forgetting rate of accuracy and sensitivity under multiple experiments are below 5%. When learning from multi-center datasets, the forgetting rates for accuracy and sensitivity decrease to 0.65% and 1.86%, making it comparable to state-of-the-art CL strategies. Through ablation experiments, we have analyzed that MP can operate with minimal storage and computational cost, which demonstrates practical potential for seizure prediction in clinical scenarios.
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Affiliation(s)
- Yufei Shi
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Shishi Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yuxuan Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Zhipeng He
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Shengsheng Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Ruixuan Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, Guangdong, China
| | - Weishi Zheng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, Guangdong, China
| | - Ziyi Chen
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yi Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
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4
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Maurya R, Mahapatra S, Dutta MK, Singh VP, Karnati M, Sahu G, Pandey NN. Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine. Sci Rep 2024; 14:17785. [PMID: 39090261 PMCID: PMC11294626 DOI: 10.1038/s41598-024-68749-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024] Open
Abstract
Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.
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Affiliation(s)
- Ritesh Maurya
- Amity Centre for Artificial Intelligence, Amity University, Noida, India
| | - Satyajit Mahapatra
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | | | - Vibhav Prakash Singh
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India
| | - Mohan Karnati
- Computer Science and Engineering Department, National Institute of Technology Raipur, Chhattisgarh, 492010, India
| | - Geet Sahu
- Amity Centre for Artificial Intelligence, Amity University, Noida, India
| | - Nageshwar Nath Pandey
- Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan, Bhubaneswar, Odisha, India
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5
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Yu H, Yang Z, Zhang Z, Wang T, Ran M, Wang Z, Liu L, Liu Y, Zhang Y. Multiple organ segmentation framework for brain metastasis radiotherapy. Comput Biol Med 2024; 177:108637. [PMID: 38824789 DOI: 10.1016/j.compbiomed.2024.108637] [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: 02/20/2024] [Revised: 04/24/2024] [Accepted: 05/18/2024] [Indexed: 06/04/2024]
Abstract
Radiotherapy is a preferred treatment for brain metastases, which kills cancer cells via high doses of radiation meanwhile hardly avoiding damage to surrounding healthy cells. Therefore, the delineation of organs-at-risk (OARs) is vital in treatment planning to minimize radiation-induced toxicity. However, the following aspects make OAR delineation a challenging task: extremely imbalanced organ sizes, ambiguous boundaries, and complex anatomical structures. To alleviate these challenges, we imitate how specialized clinicians delineate OARs and present a novel cascaded multi-OAR segmentation framework, called OAR-SegNet. OAR-SegNet comprises two distinct levels of segmentation networks: an Anatomical-Prior-Guided network (APG-Net) and a Point-Cloud-Guided network (PCG-Net). Specifically, APG-Net handles segmentation for all organs, where multi-view segmentation modules and a deep prior loss are designed under the guidance of prior knowledge. After APG-Net, PCG-Net refines small organs through the mini-segmentation and the point-cloud alignment heads. The mini-segmentation head is further equipped with the deep prior feature. Extensive experiments were conducted to demonstrate the superior performance of the proposed method compared to other state-of-the-art medical segmentation methods.
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Affiliation(s)
- Hui Yu
- College of Computer Science, Sichuan University, China
| | - Ziyuan Yang
- College of Computer Science, Sichuan University, China
| | | | - Tao Wang
- College of Computer Science, Sichuan University, China
| | - Maoson Ran
- College of Computer Science, Sichuan University, China
| | - Zhiwen Wang
- College of Computer Science, Sichuan University, China
| | - Lunxin Liu
- Department of Neurosurgery, West China Hospital of Sichuan University, China
| | - Yan Liu
- College of Electrical Engineering, Sichuan University, China.
| | - Yi Zhang
- School of Cyber Science and Engineering, Sichuan University, China
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6
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Wu Z, Su R, Dai Y, Wu X, Wu H, Wang X, Wang Z, Bao J, Chen J, Xia E. Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8 + T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity. Comput Biol Med 2024; 177:108666. [PMID: 38820773 DOI: 10.1016/j.compbiomed.2024.108666] [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: 02/02/2024] [Revised: 04/23/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND α-1,3-mannosyltransferase (ALG3) holds significance as a key member within the mannosyltransferase family. Nevertheless, the exact function of ALG3 in cancer remains ambiguous. Consequently, the current research aimed to examine the function and potential mechanisms of ALG3 in various types of cancer. METHODS Deep pan-cancer analyses were conducted to investigate the expression patterns, prognostic value, genetic variations, single-cell omics, immunology, and drug responses associated with ALG3. Subsequently, in vitro experiments were executed to ascertain the biological role of ALG3 in breast cancer. Moreover, the link between ALG3 and CD8+ T cells was verified using immunofluorescence. Lastly, the association between ALG3 and chemokines was assessed using qRT-PCR and ELISA. RESULTS Deep pan-cancer analysis demonstrated a heightened expression of ALG3 in the majority of tumors based on multi-omics evidence. ALG3 emerges as a diagnostic and prognostic biomarker across diverse cancer types. In addition, ALG3 participates in regulating the tumor immune microenvironment. Elevated levels of ALG3 were closely linked to the infiltration of bone marrow-derived suppressor cells (MDSC) and CD8+ T cells. According to in vitro experiments, ALG3 promotes proliferation and migration of breast cancer cells. Moreover, ALG3 inhibited CD8+ T cell infiltration by suppressing chemokine secretion. Finally, the inhibition of ALG3 enhanced the responsiveness of breast cancer cells to 5-fluorouracil treatment. CONCLUSION ALG3 shows potential as both a prognostic indicator and immune infiltration biomarker across various types of cancer. Inhibition of ALG3 may represent a promising therapeutic strategy for tumor treatment.
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Affiliation(s)
- Zhixuan Wu
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China; Department of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325200, China
| | - Rusi Su
- Department of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325200, China
| | - Yinwei Dai
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Xue Wu
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Haodong Wu
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Xiaowu Wang
- Department of Burns and Skin Repair Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325200, China
| | - Ziqiong Wang
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Jingxia Bao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Jiong Chen
- Department of Burns and Skin Repair Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325200, China
| | - Erjie Xia
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
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7
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Zong F, Wang L, Liu H, Xue B, Bai R, Liu Y. A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI. Comput Biol Med 2024; 175:108508. [PMID: 38678941 DOI: 10.1016/j.compbiomed.2024.108508] [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: 11/22/2023] [Revised: 04/11/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.
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Affiliation(s)
- Fangrong Zong
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
| | - Lixian Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huabing Liu
- Beijing Limecho Technology Co., Ltd., Beijing, 102200, China
| | - Bing Xue
- School of Engineering and Computer Science, Victoria University of Wellington, Victoria, 6140, New Zealand
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310020, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310030, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
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8
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Nandagopal M, Seerangan K, Govindaraju T, Abi NE, Balusamy B, Selvarajan S. A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems. Sci Rep 2024; 14:10280. [PMID: 38704423 PMCID: PMC11069552 DOI: 10.1038/s41598-024-59846-2] [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: 12/23/2023] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.
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Affiliation(s)
- Malarvizhi Nandagopal
- Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India
| | - Koteeswaran Seerangan
- Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India
| | - Tamilmani Govindaraju
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India
| | - Neeba Eralil Abi
- Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, Kerala, 682039, India
| | - Balamurugan Balusamy
- Shiv Nadar (Institution of Eminence Deemed to be University), Greater Noida, Uttar Pradesh, 201314, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Guo L, Liu L, Zhao Z, Xia X. An improved RIME optimization algorithm for lung cancer image segmentation. Comput Biol Med 2024; 174:108219. [PMID: 38581997 DOI: 10.1016/j.compbiomed.2024.108219] [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: 12/07/2023] [Revised: 02/18/2024] [Accepted: 02/25/2024] [Indexed: 04/08/2024]
Abstract
Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.
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Affiliation(s)
- Lei Guo
- Intensive Care Unit, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325088, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Zhiguang Zhao
- Department of Pathology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325088, China.
| | - Xiaodong Xia
- Department of Respiratory Medicine, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325088, China.
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10
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Gong Z, Tang Z, Qin Z, Su X, Choi C. Electrocardiogram identification based on data generative network and non-fiducial data processing. Comput Biol Med 2024; 173:108333. [PMID: 38522250 DOI: 10.1016/j.compbiomed.2024.108333] [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: 01/15/2024] [Revised: 03/01/2024] [Accepted: 03/17/2024] [Indexed: 03/26/2024]
Abstract
Nowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is fast and accurate. In this paper, we propose a linear electrocardiogram (ECG) data preprocessing algorithm based on Kalman filters for rapid noise data filtering (wavelet transform filtering algorithm). Additionally, we introduce a generative network model called Data Generation Strategy Network (DRCN) based on generative networks. The DRCN is employed to augment training samples for convolutional classification networks, ultimately improving the classification performance of the model. Through the final experiments, our method successfully reduced the average misidentification rate of ECG-based identity recognition to 2.5%, and achieved an average recognition rate of 98.7% for each category, significantly surpassing previous achievements. In the future, this method is expected to be widely applied in the field of ECG-based identity recognition.
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Affiliation(s)
- Ziyang Gong
- Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.
| | - Zhenyu Tang
- College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
| | - Zijian Qin
- College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
| | - Xin Su
- College of Information Science and Engineering, Hohai University, Changzhou, 213200, China.
| | - Chang Choi
- Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.
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11
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Zhang Y, Zhang L, Lv H, Zhang G. Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population. Front Physiol 2024; 15:1357404. [PMID: 38665596 PMCID: PMC11043598 DOI: 10.3389/fphys.2024.1357404] [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: 12/21/2023] [Accepted: 03/11/2024] [Indexed: 04/28/2024] Open
Abstract
Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors. Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors. Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model's superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model's applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846. Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.
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Affiliation(s)
- Yongsheng Zhang
- Health Management Center, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Institute of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Engineering Laboratory of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Li Zhang
- Department of Pharmacology, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Haoyue Lv
- Health Management Center, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Institute of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Engineering Laboratory of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Guang Zhang
- Health Management Center, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Institute of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Engineering Laboratory of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
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12
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Koo YR, Kim EJ, Nam IC. Development of a communication platform for patients with head and neck cancer for effective information delivery and improvement of doctor-patient relationship: application of treatment journey-based service blueprint. BMC Med Inform Decis Mak 2024; 24:81. [PMID: 38509511 PMCID: PMC10956258 DOI: 10.1186/s12911-024-02477-4] [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: 06/05/2023] [Accepted: 03/07/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Effective communication and information delivery enhance doctor-patient relationships, improves adherence to treatment, reduces work burden, and supports decision-making. The study developed a head and neck cancer (HNC) communication platform to support effective delivery of information about HNC treatment and improve the doctor-patient relationship. METHODS This study was structured in three main phases: 1) The requirement elicitation phase sought an understanding of the HNC treatment journey and service failure points (FPs) obtained through patient/medical staff interviews and observations, along with a review of the electronic health record system; 2) The development phase involved core needs analysis, solutions development through a co-creation workshop, and validation of the solutions through focus groups; and 3) the proposed HNC communication platform was integrated with the current treatment system, and the flow and mechanism of the interacting services were structured using a service blueprint (SB). RESULTS Twenty-two service FPs identified through interviews and observations were consolidated into four core needs, and solutions were proposed to address each need: an HNC treatment journey map, cancer survivor stories, operation consent redesign with surgical illustrations, and a non-verbal communication toolkit. The communication platform was designed through the SB in terms of the stage at which the solution was applied and the actions and interactions of the service providers. CONCLUSIONS The developed platform has practical significance, reflecting a tangible service improvement for both patients and medical staff, making it applicable in hospital settings.
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Affiliation(s)
- Yoo-Ri Koo
- Department of Service Design, Graduate School of Industrial Arts, Hongik University, Seoul, 04066, Korea
| | - Eun-Jeong Kim
- Department of Industry-Academic Cooperation Foundation, The Catholic University of Korea, Seoul, 06591, Korea
| | - Inn-Chul Nam
- Department of Otorhinolaryngology-Head and Neck Surgery, Incheon St. Mary's Hospital, The Catholic University of Korea, Incheon, 21431, Korea.
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13
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Gao X, Shi F, Shen D, Liu M. Multimodal transformer network for incomplete image generation and diagnosis of Alzheimer's disease. Comput Med Imaging Graph 2023; 110:102303. [PMID: 37832503 DOI: 10.1016/j.compmedimag.2023.102303] [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: 07/16/2022] [Revised: 06/27/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
Multimodal images such as magnetic resonance imaging (MRI) and positron emission tomography (PET) could provide complementary information about the brain and have been widely investigated for the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD). However, multimodal brain images are often incomplete in clinical practice. It is still challenging to make use of multimodality for disease diagnosis with missing data. In this paper, we propose a deep learning framework with the multi-level guided generative adversarial network (MLG-GAN) and multimodal transformer (Mul-T) for incomplete image generation and disease classification, respectively. First, MLG-GAN is proposed to generate the missing data, guided by multi-level information from voxels, features, and tasks. In addition to voxel-level supervision and task-level constraint, a feature-level auto-regression branch is proposed to embed the features of target images for an accurate generation. With the complete multimodal images, we propose a Mul-T network for disease diagnosis, which can not only combine the global and local features but also model the latent interactions and correlations from one modality to another with the cross-modal attention mechanism. Comprehensive experiments on three independent datasets (i.e., ADNI-1, ADNI-2, and OASIS-3) show that the proposed method achieves superior performance in the tasks of image generation and disease diagnosis compared to state-of-the-art methods.
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Affiliation(s)
- Xingyu Gao
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co.,Ltd., China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co.,Ltd., China; School of Biomedical Engineering, ShanghaiTech University, China.
| | - Manhua Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China; MoE Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China.
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14
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Hu S, Lu R, Zhu Y, Zhu W, Jiang H, Bi S. Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot. SENSORS (BASEL, SWITZERLAND) 2023; 23:7196. [PMID: 37631733 PMCID: PMC10459274 DOI: 10.3390/s23167196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges.
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Affiliation(s)
| | - Rongjian Lu
- School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (S.H.)
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15
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Pang C, Zhang Y, Xue Z, Bao J, Keong Li B, Liu Y, Liu Y, Sheng M, Peng B, Dai Y. Improving model robustness via enhanced feature representation and sample distribution based on cascaded classifiers for computer-aided diagnosis of brain disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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Shi B, Ye H, Heidari AA, Zheng L, Hu Z, Chen H, Turabieh H, Mafarja M, Wu P. Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2022; 34:4874-4887. [PMID: 38620699 PMCID: PMC8483978 DOI: 10.1016/j.jksuci.2021.09.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 09/14/2021] [Accepted: 09/18/2021] [Indexed: 01/11/2023]
Abstract
Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced new stochastic optimizer, a brain storm optimizing algorithm (EBSO), with an evolutionary machine learning algorithm called EBSO-SVM. Fast convergence and low risk of the local stagnant can be guaranteed for EBSO with added by Harris hawks optimization (HHO), and its property is verified on 23 benchmarks. Then, the EBSO is utilized to perform parameter optimization and feature selection simultaneously for support vector machine (SVM), and the presented EBSO-SVM early recognition and discrimination of COVID-19 severity in terms of coagulation indexes using COVID-19 clinical data. The classification performance of the EBSO-SVM is very promising, reaching 91.9195% accuracy, 90.529% Matthews correlation coefficient, 90.9912% Sensitivity and 88.5705% Specificity on COVID-19. Compared with other existing state-of-the-art methods, the EBSO-SVM in this paper still shows obvious advantages in multiple metrics. The statistical results demonstrate that the proposed EBSO-SVM shows predictive properties for all metrics and higher stability, which can be treated as a computer-aided technique for analysis of COVID-19 severity from the perspective of coagulation.
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Affiliation(s)
- Beibei Shi
- Affiliated People's Hospital of Jiangsu University, 8 Dianli Road, Zhenjiang, Jiangsu 212000, China
- Department of Public Health, International College, Krirk University, Bangkok 10220, Thailand
| | - Hua Ye
- Department of Pulmonary and Critical Care Medicine, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing 325600, China
| | - Ali Asghar Heidari
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Long Zheng
- Department of Pulmonary and Critical Care Medicine, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing 325600, China
| | - Zhongyi Hu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325035, China
| | - Hamza Turabieh
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Majdi Mafarja
- Department of Computer Science, Birzeit University, P.O. Box 14, West Bank, Palestine
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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Han X, Fei X, Wang J, Zhou T, Ying S, Shi J, Shen D. Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2009-2020. [PMID: 35171766 DOI: 10.1109/tmi.2022.3152157] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Transfer learning (TL) can effectively improve diagnosis accuracy of single-modal-imaging-based computer-aided diagnosis (CAD) by transferring knowledge from other related imaging modalities, which offers a way to alleviate the small-sample-size problem. However, medical imaging data generally have the following characteristics for the TL-based CAD: 1) The source domain generally has limited data, which increases the difficulty to explore transferable information for the target domain; 2) Samples in both domains often have been labeled for training the CAD model, but the existing TL methods cannot make full use of label information to improve knowledge transfer. In this work, we propose a novel doubly supervised transfer classifier (DSTC) algorithm. In particular, DSTC integrates the support vector machine plus (SVM+) classifier and the low-rank representation (LRR) into a unified framework. The former makes full use of the shared labels to guide the knowledge transfer between the paired data, while the latter adopts the block-diagonal low-rank (BLR) to perform supervised TL between the unpaired data. Furthermore, we introduce the Schatten-p norm for BLR to obtain a tighter approximation to the rank function. The proposed DSTC algorithm is evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset and the bimodal breast ultrasound image (BBUI) dataset. The experimental results verify the effectiveness of the proposed DSTC algorithm.
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18
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Liu B, Liu L, Xiao Y, Liu C, Chen X, Li W. AdaBoost-based transfer learning with privileged information. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.02.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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19
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Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design. MATHEMATICS 2022. [DOI: 10.3390/math10020276] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.
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20
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Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction. ELECTRONICS 2022. [DOI: 10.3390/electronics11020209] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.
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21
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Zhao S, Wang P, Heidari AA, Chen H, He W, Xu S. Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy. Comput Biol Med 2021; 139:105015. [PMID: 34800808 DOI: 10.1016/j.compbiomed.2021.105015] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 11/30/2022]
Abstract
Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com.
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Affiliation(s)
- Songwei Zhao
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Pengjun Wang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Wenming He
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.
| | - Suling Xu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.
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22
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23
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Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 2021; 138:104910. [PMID: 34638022 DOI: 10.1016/j.compbiomed.2021.104910] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 01/11/2023]
Abstract
Breast cancer is one of the most dangerous diseases for women's health, and it is imperative to provide the necessary diagnostic assistance for it. The medical image processing technology is one of the most critical of all complementary diagnostic technologies. Image segmentation is the core step of image processing, where multilevel image segmentation is considered one of the most efficient and straightforward methods. Many multilevel image segmentation methods based on evolutionary and population-based methods have been proposed in recent years, but many have the fatal weakness of poor convergence accuracy and the tendency to fall into local optimum. Therefore, to overcome these weaknesses, this paper proposes a modified differential evolution (MDE) algorithm with a vision based on the slime mould foraging behavior, where the recently proposed slime mould algorithm (SMA) inspires it. Besides, to obtain high-quality breast cancer image segmentation results, this paper also develops an excellent MDE-based multilevel image segmentation model, the core of which is based on non-local means 2D histogram and 2D Kapur's entropy. To effectively validate the performance of the proposed method, a comparison experiment between MDE and its similar algorithms was first carried out on IEEE CEC 2014. Then, an initial validation of the MDE-based multilevel image segmentation model was performed by utilizing a reference image set. Finally, the MDE-based multilevel image segmentation model was compared with peers using breast invasive ductal carcinoma images. A series of experimental results have proved that MDE is an evolutionary algorithm with high convergence accuracy and the ability to jump out of the local optimum, as well as effectively demonstrated that the developed model is a high-quality segmentation method that can provide practical support for further research of breast invasive ductal carcinoma pathological image processing.
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Qiao Z, Shan W, Jiang N, Heidari AA, Chen H, Teng Y, Turabieh H, Mafarja M. Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns. INT J INTELL SYST 2021. [DOI: 10.1002/int.22658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Zenglin Qiao
- School of Emergency Management, Institute of Disaster Prevention Langfang China
| | - Weifeng Shan
- School of Emergency Management, Institute of Disaster Prevention Langfang China
- Institute of Geophysics, China Earthquake Administration Beijing China
| | - Nan Jiang
- College of Information Engineering, East China Jiaotong University Nanchang Jiangxi China
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Yuntian Teng
- Institute of Geophysics, China Earthquake Administration Beijing China
| | - Hamza Turabieh
- Department of Information Technology College of Computers and Information Technology, Taif University Taif Saudi Arabia
| | - Majdi Mafarja
- Department of Computer Science Birzeit University West Bank Palestine
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Shi B, Ye H, Zheng L, Lyu J, Chen C, Heidari AA, Hu Z, Chen H, Wu P. Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine. Comput Biol Med 2021; 136:104698. [PMID: 34426165 PMCID: PMC8323529 DOI: 10.1016/j.compbiomed.2021.104698] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/20/2021] [Accepted: 07/23/2021] [Indexed: 12/22/2022]
Abstract
Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.
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Affiliation(s)
- Beibei Shi
- Affiliated People's Hospital of Jiangsu University, 8 Dianli Road, Zhenjiang, Jiangsu, 212000, China.
| | - Hua Ye
- Department of Pulmonary and Critical Care Medicine, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, 325600, China.
| | - Long Zheng
- Department of Pulmonary and Critical Care Medicine, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, 325600, China.
| | - Juncheng Lyu
- Weifang Medical University School of Public Health, China.
| | - Cheng Chen
- Center of Clinical Research, Wuxi People's Hospital of Nanjing Medical University, Wuxi, Jiangsu, 214023, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Zhongyi Hu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis. Comput Biol Med 2021; 135:104582. [PMID: 34214940 DOI: 10.1016/j.compbiomed.2021.104582] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/13/2021] [Accepted: 06/13/2021] [Indexed: 02/05/2023]
Abstract
Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.
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Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J. Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review. J Imaging 2021; 7:66. [PMID: 34460516 PMCID: PMC8321322 DOI: 10.3390/jimaging7040066] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 11/25/2022] Open
Abstract
(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.
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Affiliation(s)
| | | | | | | | | | | | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland; (J.M.V.); (V.I.); (A.A.); (R.D.F.); (M.P.); (R.C.)
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Wang Y, Wang L, Yang Y, Lian T. SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection. EXPERT SYSTEMS WITH APPLICATIONS 2021; 166:114090. [PMID: 33041529 DOI: 10.1016/j.eswa.2021.114864] [Citation(s) in RCA: 274] [Impact Index Per Article: 91.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/17/2020] [Accepted: 10/02/2020] [Indexed: 05/27/2023]
Abstract
The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods.
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Affiliation(s)
- Yuhang Wang
- Data Science College, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, China
| | - Li Wang
- Data Science College, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, China
| | - Yanjie Yang
- Data Science College, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, China
| | - Tao Lian
- Data Science College, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, China
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