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Deep Learning and MRI Biomarkers for Precise Lung Cancer Cell Detection and Diagnosis

Open Bioinformatics Journal 2024年 17卷

作者： Kumar, Sandeep Singh, Jagendra Ravi, Vinayakumar Singh, Prabhishek Mazroa, Alanoud Al Diwakar, Manoj Gupta, Indrajeet Department of Computer Science & Engineering Maharaja Surajmal Institute of Technology Delhi India School of Computer Science Engineering & Technology Bennett University Greater Noida India CSE Department Graphic Era deemed to be University Uttarakhand Dehradun India School of Computer Science & AI SR University Telangana Warangal 506371 India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Aim: This research work aimed to combine different AI methods to create a modular diagnosis system for lung cancer, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), VGG16, and Recurrent Neural Network (RNN) on MRI biomarkers. Models have then been evaluated and compared in their effectiveness in detecting cancer, using a meticulously selected dataset containing 2045 MRI images, with emphasis being put on documenting the benefits of the multimodal approach for attacking the complexities of the disease. Background: Lung cancer remains the most common cause of cancer death in the world, partly because of the challenges in diagnosis and the late stage of presentation. Although Magnetic Resonance Imaging (MRI) has become a critical modality in the identification and staging of lung cancer, too often, its effectiveness is curtailed by the interpretative variance among radiologists. Recent advances in machine learning hold great promise for augmenting the analysis of MRI and perhaps even increasing diagnostic accuracy with the start of timely treatment. In this work, the integration of advanced machine learning models with MRI biomarkers to solve these problems has been investigated. Objective: The purpose of the present paper was to assess the effectiveness of integrating various machine-learning models with MRI biomarkers for lung cancer diagnostics, such as CNN, KNN, VGG16, and RNN. The dataset involved 2,045 MRI images, and the performances of the models were investigated by comparing their performance metrics to determine the best configuration of interconnection while underpinning the necessity of this multimodal approach for accurate diagnoses and, consequently, better patient outcomes. Methods: For this study, we used 2045 MRI images, with 70% for training and 30% for validation. We used four machine-learning models to work on the photos: CNN, KNN, VGG16, and RNN. Systematic performance measures were included in the study: accuracy, recall,

关键词： Diagnostic accuracy Ensemble models Lung cancer Machine learning Magnetic resonance imaging

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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EMPIRICAL NETWORK STRUCTURE OF MALICIOUS PROGRAMS

arXiv

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arXiv 2022年

作者： Musgrave, John Campan, Alina Messay-Kebede, Temesguen Kapp, David Ralescu, Anca Department of Computer Science University of Cincinnati CincinnatiOH United States Department of Computer Science College of Informatics Northern Kentucky University Highland HeightsKY United States Air Force Research Lab Wright-Patt Air Force Base DaytonOH United States

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program’s operational semantics to its structure. © 2022, CC BY-NC-ND.

关键词： Semantics

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Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

引用

computer Modeling in Engineering & sciences 2020年第10期125卷 315-344页

作者： Nazish Naheed Muhammad Shaheen Sajid Ali Khan Mohammed Alawairdhi Muhammad Attique Khan Faculty of Engineering&Information Technology Foundation University IslamabadIslamabadPakistan College of Computing and Informatics Saudi Electronic UniversityRayadhSaudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan

In the area of pattern recognition and machine learning,features play a key role in *** famous applications of features are medical imaging,image classification,and name a few *** the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of *** data repositories contain details information essential to support medical diagnostic decisions and also improve patient care *** the other hand,this growth also made it difficult to comprehend and utilize data for various *** results of imaging data can become biased because of extraneous features present in larger *** selection gives a chance to decrease the number of components in such large *** selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization *** correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast *** paper presents a review of feature selection techniques and attributes selection measures for medical *** review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining *** review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced ***,this review provides the importance of feature selection for correct classification of medical *** the end,critical analysis and future directions are provided.

关键词： Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

SSRN

引用

SSRN 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spa-tial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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CNN-based Transfer Learning for Covid-19 Diagnosis

CNN-based Transfer Learning for Covid-19 Diagnosis

引用

2021 International Conference on Information Technology, ICIT 2021

作者： Ahmed, Zanear Sh. Surameery, Nigar M. Shafiq Rashid, Rasber Dh. Salih, Shadman Q. Abdulla, Hawre Kh. Erbil Technology College Erbil Polytechnic University Information Technology Department Kurdistan Region Erbil Iraq College of Engineering University of Garmian Civil Engineering Department Sulaimani Kurdistan Region Kalar Iraq College of Science Salahaddin University Computer Science and IT Department Kurdistan Region Erbil Iraq Technical College of Informatics Sulaimani Polytechnic University Database Technology Department Kurdistan Region Sulaimani Iraq

ISBN: (纸本)9781665428705

CNN-based transfer learning method plays a significant role in the detection of various objects such as cars, dogs, motorcycles, face and human detection in nighttime images by using visible light camera sensors. This method mainly depends on the images captured by cameras in order to detect the mentioned objects in a variety of environments based on convolutional neural networks (CNNs). In this study, we utilized the same method to detect coronavirus phenomena by using chest X-ray images that have been collected from three different open-source datasets with the aim of rapid detection of the infected patients and speed up the diagnostic process. We used one of the deep learning architectures in a Transfer Learning mode and modified its final layers to adapt to the number of classes in our investigation. The deep learning architecture that we used for the purpose of COVID-19 detection from X-ray images is a CNN designed to detect human in nighttime. We also modified the CNN architecture in three different scenarios named (Model 1, Model 2 and Model 3) in order to improve the classification results. Compared to model one and two, the result improved in model three and the number of misclassified cases reduced particularly in detecting Abnormal and COVID-19 cases. Although our CNN-based method shows high performance in COVID-19 detection, CNN decisions should not to be taken into consideration until clinical tests confirms symptoms of the infected patients. © 2021 IEEE.

关键词： COVID-19

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Monitoring Earth Hazard with Remote Sensing Techniques

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Photogrammetric Engineering and Remote Sensing 2022年第2期88卷 92-92页

作者： Malarvizhi Kumar, Priyan Afghah, Fatemeh Arunmozhi, Manimuthu Department of Computer Science and Engineering Kyung Hee University Korea Republic of School of Informatics Computing and Cyber Systems Northern Arizona University United States Nanyang Technological University Singapore

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Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins

arXiv

引用

arXiv 2022年

作者： Zhang, Nan Bahsoon, Rami Tziritas, Nikos Theodoropoulos, Georgios Shenzhen China School of Computer Science University of Birmingham Birmingham United Kingdom Research Institute for Trustworthy Autonomous Systems Shenzhen China Department of Informatics and Telecommunications University of Thessaly Greece

Digital Twins (DT) are essentially dynamic data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and reconfiguration aimed at more accurate modelling and analysis in DT. The reconfiguration decisions can be autonomous or interactive, keeping human-in-the-loop. The trustworthiness of these decisions can be hindered by inadequate explainability of the rationale, and utility gained in implementing the decision for the given situation among alternatives. Additionally, different decision-making algorithms and models have varying complexity, quality and can result in different utility gained for the model. The inadequacy of explainability can limit the extent to which humans can evaluate the decisions, often leading to updates which are unfit for the given situation, erroneous, compromising the overall accuracy of the model. The novel contribution of this paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback. The approach utilises interpretable machine learning and goal modelling to explainability, and considers trade-off analysis of utility gained. We use examples from smart warehousing to demonstrate the approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

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Advanced Interoperable Framework for Real-Time Predictive Analysis Leveraging Machine Learning and IoT in Smart Health Monitoring systems

Advanced Interoperable Framework for Real-Time Predictive An...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： D. Antony Pradeesh Y. Syamala P. Priya Madhavi Sripathi Srilakshmi Kaza Rohith Bala Jaswanth B Department of Electronics and Communication Systems KG College of Arts and Science Coimbatore India Department of IoT Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh Department of Computer Science The Quaide Milleth College for Men Chennai Tamilnadu Department of Business and Management Studies Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh Department of Electronics and Communication Engineering Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh

As health care evolves rapidly, devices enabled by the Internet of Things, and algorithms driven by Machine Learning have surfaced as transformative elements. However, these technologies often fall short due to a lack of interoperability between existing systems, real-time predictive capabilities, and strong security measures. This paper presents a novel framework designed to overcome these challenges and optimize Internet of Things applications in health monitoring systems. Our framework has been specifically created to work seamlessly across the Internet of Things devices while monitoring a wide array of health parameters. It integrates advanced machine learning models, including Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting, Random Forest and Neural Network to improve prediction accuracy. The framework also allows real-time analysis and prediction services with real-time updates available through an Internet portal. Furthermore, we employ robust security and privacy measures to safeguard patient data. Experimental results illustrate our framework's superior performance over existing systems, demonstrating its promise for creating efficient, secure smart health monitoring systems.

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A Novel Approach to Menstrual Cycle Prediction Using GAN-Generated Data and Transformer Model for Tabular Data

A Novel Approach to Menstrual Cycle Prediction Using GAN-Gen...

引用

IEEE International WIE Conference on Electrical and computer Engineering (WIECON-ECE)

作者： Md Younus Ahamed Md Asif Bin Syed Mafuza Nasrin Tani Tanpia Tasnim Al Zadid Sultan Bin Habib Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Department of Industrial and Management Systems Engineering West Virginia University Morgantown WV USA Army Medical College Rangpur Rangpur Bangladesh Green University of Bangladesh Narayanganj Dhaka Bangladesh

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Accurate menstrual cycle prediction is crucial for women's health and fertility management. While prior studies have utilized machine learning models to predict cycle length and classify regularity, they often struggled with the low Observationto-Variable Ratio (OVR) inherent in menstrual cycle data. This study tackles this issue by employing Generative Adversarial Networks (GANs) to generate synthetic data and Transformerbased models for both classification a nd r egression t asks. We evaluated multiple machine learning models for menstrual cycle phase classification a nd l ength p rediction. F or t he classification of regular versus irregular cycles, TabNet significantly outperformed baseline models, including Logistic Regression, Random Forest, and LSTM, achieving 99.67% accuracy, an AUC of 0.96, an F1 score of 0.99, 92.37% specificity, a nd 99.66% sensitivity, with CTGAN-generated synthetic data enhancing its performance. In the regression task of predicting cycle length, TabNet demonstrated strong predictive power, with an MSE of 1.29, RMSE of 1.13, and an R 2 score of 0.87. Although Random Forest performed slightly better with an MSE of 0.958 and an R 2 score of 0.90, TabNet remained competitive. These results highlight the potential of GAN-generated data and advanced models like TabNet in overcoming low OVR challenges and improving menstrual cycle prediction. Future research may incorporate personalized health data to further enhance model accuracy and generalizability.

关键词： Logistic regression Accuracy Sensitivity Medical services Predictive models Transformers Data models Random forests Long short term memory Synthetic data

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