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Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

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Journal of Bionic Engineering 2023年第5期20卷 2240-2275页

作者： Jiaochen Chen Zhennao Cai Huiling Chen Xiaowei Chen josé escorcia-gutierrez Romany F.Mansour Mahmoud Ragab College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Department of Rheumatology and Immunology The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China Department of Computational Science and Electronics Universidad de la CostaCUC080002BarranquillaColombia Department of Mathematics Faculty of ScienceNew Valley University72511El-KhargaEgypt Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing *** assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN *** method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS *** DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 *** addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological *** results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal *** to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation *** research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

关键词： Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm Swarm intelligence algorithms Bionic algorithm

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： joséescorcia-gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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Computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti joséescorcia-gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

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Computers, Materials & Continua 2021年第11期69卷 2031-2044页

作者： K.Muthumayil S.Manikandan S.Srinivasan joséescorcia-gutierrez Margarita Gamarra Romany F.Mansour Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Biomedical Engineering Saveetha School of EngineeringSIMATSChennai602105India Electronic and Telecommunicacions Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human *** spite of its prevalence and serious consequences,it is mostly diagnosed through manual *** risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual ***,there is a need exists for automatic diagnosis and classification method that can replace the manual *** is mainly classified into acute and chronic *** current research work proposed a computer-based application to classify the *** the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence *** study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)*** proposed method achieved optimum accuracy in detection and classification of leukemia from WBC ***,the study results establish the superiority of the proposed method in automated diagnosis of *** values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,***,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.

关键词： White blood cells enhanced virtual neural networking segmentation feature extraction chronic lymphocytic leukemia

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Automated Deep Learning Empowered Breast Cancer Diagnosis UsingBiomedical Mammogram Images

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Computers, Materials & Continua 2022年第6期71卷 4221-4235页

作者： joséescorcia-gutierrez Romany F.Mansour Kelvin Belen Javier Jiménez-Cabas Meglys Pérez Natasha Madera Kevin Velasquez Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Mechatronics Engineering Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic *** the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging *** mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death *** the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false *** resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital *** goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital *** proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image *** addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction ***,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 *** wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast *** ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance *** simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

关键词： Breast cancer digital mammograms deep learning wavelet neural network Resnet 34 disease diagnosis

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Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

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Computer Systems Science & Engineering 2023年第8期46卷 1337-1353页

作者： josé escorcia-gutierrez Roosvel Soto-Diaz Natasha Madera Carlos Soto Francisco Burgos-Florez Alexander Rodríguez Romany F.Mansour Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Mechatronics Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Medicine Universidad del NorteBarranquilla081007Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and *** the same time,CXR interpretation is a time-consuming and subjective ***,high resemblance among the radiological patterns of TB and other lung diseases can result in ***,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB *** this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR ***,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image ***,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature *** addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the *** performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many *** experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

关键词： Computer-aided diagnosis water strider optimization deep learning chest x-rays transfer learning

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Mitochondria–cGAS–STING axis is a potential therapeutic target for senescence-dependent inflammaging-associated neurodegeneration

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Neural Regeneration Research 2025年第3期20卷 805-807页

作者： joséM.Izquierdo Centro de Biología Molecular Severo Ochoa(CBM) Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid(CSIC-UAM)MadridSpain

The word “senescence” comes from the Latin senescens, meaning “to begin to age”, and is characterized by a long-lasting but reversible block in proliferation, resulting from stress-induced cell cycle arrest of pre... 详细信息

关键词： Latin potential lasting

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A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

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Computers, Materials & Continua 2022年第2期70卷 2971-2989页

作者： jose escorcia-gutierrez Jordina Torrents-Barrena Margarita Gamarra Natasha Madera Pedro Romero-Aroca Aida Valls Domenec Puig Electronic and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Ophthalmology Service Universitari Hospital Sant JoanInstitut de Investigacio Sanitaria Pere VirgiliReus43201Spain Departament d’Enginyeria Informàtica i Matemàtiques Escola Tècnica Superior d’EnginyeriaUniversitat Rovira i VirgiliTarragona43007Spain

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational *** method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

关键词： Diabetic retinopathy artificial neural networks decision trees support vector machines feature selection retinal vasculature segmentation

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The emerging role of mesenchymal stem cell-derived extracellular vesicles to ameliorate hippocampal NLRP3 inflammation induced by binge-like ethanol treatment in adolescence

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Neural Regeneration Research 2025年第4期20卷 1153-1163页

作者： Susana Mellado María joséMorillo-Bargues Carla Perpiñá-Clérigues Francisco García-García Victoria Moreno-Manzano Consuelo Guerri María Pascual Department of Physiology School of Medicine and DentistryUniversity of ValenciaValenciaSpain Bioinformatics and Biostatistics Unit Príncipe Felipe Research CenterValenciaSpain Neuronal and Tissue Regeneration Laboratory Príncipe Felipe Research CenterValenciaSpain

Our previous studies have reported that activation of the NLRP3(NOD-,LRR-and pyrin domain-containing protein 3)-inflammasome complex in ethanol-treated astrocytes and chronic alcohol-fed mice could be associated with neuroinflammation and brain *** stem cell-derived extracellular vesicles(MSC-EVs)have been shown to restore the neuroinflammatory response,along with myelin and synaptic structural alterations in the prefrontal cortex,and alleviate cognitive and memory dysfunctions induced by binge-like ethanol treatment in adolescent *** the therapeutic role of the molecules contained in mesenchymal stem cell-derived extracellular vesicles,the present study analyzed whether the administration of mesenchymal stem cell-derived extracellular vesicles isolated from adipose tissue,which inhibited the activation of the NLRP3 inflammasome,was capable of reducing hippocampal neuroinflammation in adolescent mice treated with binge *** demonstrated that the administration of mesenchymal stem cell-derived extracellular vesicles ameliorated the activation of the hippocampal NLRP3 inflammasome complex and other NLRs inflammasomes(e.g.,pyrin domain-containing 1,caspase recruitment domain-containing 4,and absent in melanoma 2,as well as the alterations in inflammatory genes(interleukin-1β,interleukin-18,inducible nitric oxide synthase,nuclear factor-kappa B,monocyte chemoattractant protein-1,and C–X3–C motif chemokine ligand 1)and miRNAs(miR-21a-5p,miR-146a-5p,and miR-141-5p)induced by binge-like ethanol treatment in adolescent *** analysis further revealed the involvement of miR-21a-5p and miR-146a-5p with inflammatory target genes and NOD-like receptor signaling *** together,these findings provide novel evidence of the therapeutic potential of MSC-derived EVs to ameliorate the hippocampal neuroinflammatory response associated with NLRP3 inflammasome activation induced by binge drinking in adolescence.

关键词： adolescence binge-like ethanol treatment extracellular vesicles hippocampus mesenchymal stem cells neuroinflammation NOD-,LRR-and pyrin domain-containing protein 3(NLRP3)

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Preferences for pre-exposure prophylaxis among U.S. military men who have sex with men:results of an adaptive choice based conjoint analysis study

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Military Medical Research 2022年第2期9卷 141-151页

作者： joséI.gutierrez Alex Dubov Frederick L.Altice David Vlahov Yale School of Nursing 400 West Campus DriveOrangeCT 06477USA Philip R.Lee Institute for Health Policy Studies University of CaliforniaSan Francisco3333 California StSan FranciscoCA 94118USA Loma Linda University Griggs Hall 11065Loma LindaCA 92350USA Yale School of Medicine Section of Infectious Diseases135 College StreetSuite 323New HavenCT 06510USA Department of Epidemiology-Microbial Diseases Yale School of Public Health60 College StNew HavenCT 06510USA

Background: Pre-exposure prophylaxis(PrEP) prevents human immunodeficiency virus(HIV) infection, but its use remains low among U.S. military men who have sex with men(MSM), likely due to mis-matching with personal preferences. We conducted a study to characterize preferences to PrEP measures within this ***: HIV-negative military MSM were recruited through a closed, Lesbian, Gay, Bisexual, and Transgendered(LGBT) military social media group. The survey was anonymous, and consisted of five experimentally varied attributes in service delivery: dosing method, provider type, visit location, lab work evaluation location, and dispensing *** importance and part-worth utility scores were generated using hierarchical bayes(HB) estimation, and the randomized first choice model was used to examine participation interest across eight possible PrEP program ***: A total of 429 participants completed the survey. Among the eight scenarios with varying attributes, the most preferred scenario featured a daily tablet, PrEP injection or implant, along with a military provider, smartphone/telehealth visit, and on-base locations for lab evaluation and medication pick-up. The results also emphasized the importance for providers to be familiar with PrEP prescription knowledge, and to provide interactions sensitive to sexual identity and mental ***: A PrEP program consisting of daily tablet is preferred in military healthcare settings is preferred. Longacting implants and injections are also desired.

关键词： Conjoint analysis Pre-exposure prophylaxis Preference Decision science Human immunodeficiency virus Military health Infectious disease

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