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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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Computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of electrical and Electronics engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and information engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia school of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer engineering and information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

Ultra‑Transparent and Multifunctional IZVO Mesh Electrodes for Next‑Generation Flexible Optoelectronics

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Nano-Micro Letters 2025年第1期17卷 293-309页

作者： Kiran A.Nirmal Tukaram D.Dongale Atul C.Khot Chenjie Yao Nahyun Kim Tae Geun Kim school of electrical engineering Korea UniversityAnam‑ro 145Seongbuk‑guSeoulRepublic of Korea Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India

Mechanically durable transparent electrodes are essential for achieving long-term stability in flexible optoelectronic ***,they are crucial for applications in the fields of energy,display,healthcare,and soft *** meshes represent a promising alternative to traditional,brittle,metal oxide conductors due to their high electrical conductivity,optical transparency,and enhanced mechanical *** this paper,we present a simple method for fabricating an ultra-transparent conducting metal oxide mesh electrode using selfcracking-assisted *** this method,we produced an electrode with ultra-transparency(97.39%),high conductance(Rs=21.24Ωsq^(−1)),elevated work function(5.16 eV),and good mechanical *** also evaluated the effectiveness of the fabricated electrodes by integrating them into organic photovoltaics,organic light-emitting diodes,and flexible transparent memristor devices for neuromorphic computing,resulting in exceptional device *** addition,the unique porous structure of the vanadium-doped indium zinc oxide mesh electrodes provided excellent flexibility,rendering them a promising option for application in flexible optoelectronics.

关键词： Self-cracking template Vanadium-doped indium zinc oxide mesh Organic solar cells Organic light-emitting diodes Flexible transparent memory

Effects of treatment time and temperature on the DC corona pretreatment performance of waste activated sludge

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Plasma Science and Technology 2018年第2期20卷 134-143页

作者：高宇赵宁邓永迪王明行杜伯学 school of electrical and information engineering Tianjin University

In order to improve the anaerobic digestion efficiency of waste activated sludge（WAS）,a pretreatment procedure should be carried out so as to disrupt the microbial cell structure,thus releasing intracellular organic *** this paper,a corona discharge triggered by a DC voltage was employed to pre-treat WAS for various time periods under different *** magnitude of the DC voltage was 4 k V at both negative and positive *** changes in the soluble chemical oxygen demand,phosphorus and nitrogen content,and p H value within the WAS were utilized to estimate the pretreatment performance of the DC *** was found that with increasing treatment time,the pretreatment efficiency tends to be *** increased temperature,the pretreatment efficiency appears to be *** is suggested that the oxidative species and the active particles generated in the corona discharge play an important role in disrupting the microbial cell structure,which is dependent upon the treatment time and the temperature.

关键词： waste activated sludge anaerobic digestion microbial cell DC corona soluble chemical oxygen demand phosphorus nitrogen pH value

CGNet:cross-guidance network for semantic segmentation

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Science China(information Sciences) 2020年第2期63卷 49-64页

作者： Zhijie ZHANG Yanwei PANG school of electrical and information engineering Tianjin University

Semantic segmentation is a fundamental task in image analysis. The issue of semantic segmentation is to extract discriminative features for distinguishing different objects and recognizing hard ***, most existing methods have limitations on resolving this problem. To tackle this problem, we identify the contributions of the edge and saliency information for segmentation and present a novel end-to-end network, termed cross-guidance network(CGNet) to leverage them to benefit the semantic segmentation. The edge and saliency detection network are unified into the CGNet, and model the intrinsic information among them, guiding the process of extracting discriminative features. Specifically, the CGNet attempts to extract segmentation, edge, and salient features, simultaneously. Then it transfers them into the cross-guidance module(CGM) to generate the pre-knowledge features based on the modeled information, optimizing the context feature extraction process. The proposed approach is extensively evaluated on PASCAL VOC 2012,PASCAL-Person-Part, and Cityscapes, and achieves state-of-the-art performance, demonstrating the superiority of the proposed approach.

关键词： semantic segmentation fully convolutional networks pyramid network edge detection saliency detection cross-guidance

Troubleshooting algorithm for solving assignment problem and its applications

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Journal of Systems engineering and Electronics 2013年第1期24卷 165-172页

作者： Li Zhou Hailin Zou Yancun Yang Qian Gao school of information and electrical engineering Ludong University

A new troubleshooting algorithm for solving assignment problem based on existing algorithms is proposed, and an analysis on the related theory is given. By applying the new troubleshooting algorithm to the Lagrange relaxation algorithm of the multi-dimensional assignment problem of data association for multi-passive-sensor multi-target location systems, and comparing the simulation results with that of the Hungarian algorithm which is the classical optimal solving algorithm, and the multi-layer ordersearching algorithm which is a sub-optimal solving algorithm, the performance and applying conditions of the new algorithm are summarized. Theory analysis and simulation results prove the effectiveness and superiority of the new algorithm.

关键词： assignment problem troubleshooting algorithm data association.

Joint College Admissions Game and Auction Theory for Data Offloading in Heterogeneous Networks

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Chinese Journal of Electronics 2018年第1期27卷 168-174页

作者： WANG Gang LIU Peizhen YANG Zhao XUE Rui school of electrical and information engineering Beihang University

Mobile data offloading through third-party Femtocell access points(FAPs) is an emerging technology which is used to alleviate congestion in cellular *** propose a distributed algorithm that combines college admissions game with auction theory to achieve the maximum social welfare of offloading and guarantee the Quality of service(Qo S) of Mobile users(MUs). As the optimal problem is NP-hard, the college admissions game is developed to solve the sub-problem of matching between FAPs and MUs. The Second revenue sealed-bid auction with a reserve price(SRSARP) algorithm is developed to solve another sub-problem of matching between Wireless operators(WOs) and FAPs. The simulation results reveal that the distributed algorithm we proposed achieves the performance very close to that of the centralized method and significantly better than those of the random and Maxprice algorithms.

关键词： Data offloading College admissions game Auction theory

A hybrid strategy to control uncertain nonlinear chaotic system

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Chinese Physics B 2017年第10期26卷 89-97页

作者：隋永波何怡刚于文新李燕 The school of electrical and Automation engineering Hefei University of Technology school of information and electrical engineering Hunan University of Science and Technology College of electrical and information engineering Hunan University

In this paper, a new method, based on firefly algorithm （FA） and extreme learning machine （ELM）, is proposed to control chaos in nonlinear system. ELM is an efficient predicted and classified tool, and can match and fit nonlinear systems efficiently. Hence, mathematical model of uncertain nonlinear system is obtained indirectly. For higher fitting accuracy, a novel swarm intelligence algorithm FA is drawn in our proposed way. The main advantage is that our proposed method can remove the limitation that mathematical model must be known clearly and can be applied to unknown nonlinear chaotic system.

关键词： chaos firefly algorithm extreme learning machine

Fuzzy Static Output Feedback H_∞ Control for Nonlinear Systems Subject to Parameter Uncertainties

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Journal of Systems Science & Complexity 2018年第2期31卷 343-371页

作者： ZHAO Tao DIAN Songyi school of electrical engineering and information Sichuan University

This study is concerned with the stabilization issue of nonlinear systems subject to parameter uncertainties. An interval type-2 T-S fuzzy model is used to represent the nonlinear systems subject to parameter uncertainties. An interval type-2 fuzzy static output feedback controller is designed to synthesize the interval type-2 T-S fuzzy systems. The membership-function-dependent stability conditions are derived by utilizing the information of upper and lower membership functions. The proposed stability conditions are presented in the form of linear matrix inequalities（LMIs）. LMI-based stability conditions for interval type-2 fuzzy static output feedback H_∞ control synthesis are also *** simulation examples are given to show the superiority of the proposed approach.

关键词： Interval type-2 fuzzy controller parameter uncertainties static output feedback control T-S fuzzy systems.