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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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GuardianForest: Cloud-Powered Patient Health Assurance through Isolation Forests 7

GuardianForest: Cloud-Powered Patient Health Assurance throu...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Subramanian, M. Gokulakrishnan, K. Nirmala, B. Vijaya Vijay Shankar, S. Krishnan, R. Santhana Vinoth Rajkumar, G. Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India College of Engineering Guindy Anna University Department of Electronics And Communication Engineering Chennai India Psna College of Engineering And Technology Department of Information Technology Dindigul India Sona College of Technology Department of Electrical And Electronics Engineering Salem India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India J.P. College of Engineering Department of Electronics And Communication Engineering Thenkasi India

ISBN: (纸本)9798350359299

The proposed patient monitoring system is a substantial progression in healthcare technology. It integrates various sensors and communication modules to provide real-time patient data to caregivers and medical professionals. The array of sensors includes a non-contact temperature sensor, a rain sensor, a Max30100 sensor, and a load cell sensor. The non-contact temperature sensor vigilantly tracks the patient's body temperature, alerting caregivers to any deviations. The rain sensor serves as a communication channel for patients unable to express needs, detecting urination. The Max30100 sensor monitors critical health metrics like heart rate and blood oxygen levels for early detection of conditions. The load cell sensor observes saline levels in IV bags, triggering alerts on nearing critical thresholds. Powered by an Arduino microcontroller, the system aggregates data, relaying it to a dedicated server via Wi-Fi for remote accessibility. The Isolation Forest-based IsoSentry Algorithm enhances anomaly detection, identifying aberrations in health readings. Adding reliability, a GSM module connected to Arduino dispatches real-time alerts via text messages. In essence, "GuardianForest"epitomizes a transformative leap in healthcare, integrating cutting-edge hardware, wireless communication, and cloud-based data management to enhance overall healthcare responsiveness and efficacy. © 2024 IEEE.

关键词： Internet of things

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Hybrid Glowworm-Fish Swarm Optimization with Fuzzy SVM for Enhanced Ovarian Cancer Detection 5

Hybrid Glowworm-Fish Swarm Optimization with Fuzzy SVM for E...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajan, S. Palanivel Premkumar, R. Department of Electronics and Communication Engineering Viraganoor Madurai India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Thottiam Tiruchirappalli India

ISBN: (纸本)9798350389609

This research work presents an attempt to develop a new optimization algorithm known as Hybrid Glowworm Swarm Optimization with Fish Swarm Optimization and Fuzzy Support Vector Machine which can be used for improving the accuracy rate for detection of ovarian cancer based on computer aided diagnosis. The hybrid approach extracts the strong features of both Glowworm Swarm Optimization (GSO) and Fish Swarm Optimization (FSO) on the feature selection process while the Fuzzy Support Vector Machine (FSVM) is used for the classification process. GSO and FSO were used together in order to enhance the search ability and the rate of convergence, to cope with the feature space of ovarian cancer datasets that might be complicated. The FSVM carries this kind of classifications further and addresses inherent uncertainties and overlapping features within data. This integrated method tries to offer more frequent and accurate tool for early detection and may help improve the condition of patients with ovarian cancer. The proposed HGSO-FSO-FSVM framework is yet to be implemented but expected to devise major steps towards enhancement of medical diagnostics especially in early diagnose of ovarian cancer which is crucial in increasing survival rate. © 2024 IEEE.

关键词： Diseases

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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An Initial Solution Based Selective Harmonic Elimination Method for Multilevel Inverter

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power electronics and Applications 2024年第1期9卷 63-78页

作者： Kala, Peeyush Sharma, Abhinav Jately, Vibhu Joshi, Jyoti Yang, Yongheng Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Ghaziabad201204 India Department of Electrical and Electronics Engineering Dehradun248007 India Dehradun248007 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun248002 India College of Electrical Engineering Zhejiang University Hangzhou310027 China

This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of iterations. Initial SHE solution is defined as a set of initial switching angles that has less accuracy as compared to the final SHE solution. To find initial SHE solution, tunicate swarm algorithm (TSA), grey wolf optimization (GWO) and whale optimization (WO) are used to solve five-dimensional SHE problem. A comparative analysis demonstrates that TSA achieved the desired initial SHE solution within lesser iterations as compared to GWO and WO for a wide range of modulation indices. The initial switching angles found using TSA are further optimized using Newton-Raphson (NR) method being the proposed TSANR method, which exploits the SHE search space with quadratic rate of convergence to find final SHE solution. The proposed TSANR method significantly minimizes the computational time and further improves the accuracy of the SHE solution as compared to the state-of-the-art SHE methods. Detailed simulations, experimental and harmonic analysis under dynamic change in modulation index are presented to show the efficacy of the proposed SHE method in terms of control over fundamental and detrimental harmonics. © 2017 CPSS.

关键词： Harmonic analysis

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A Comb-shaped Corrugated Antipodal Vivaldi Antenna with a Parasitic Patch for UWB Application 3

A Comb-shaped Corrugated Antipodal Vivaldi Antenna with a Pa...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Razib, Mohammad Rahman Das, Debprosad Murshed, Abu Hena Istiaque, Arif Hossain, Md. Azad Chittagong University of Engineering & Technology Department of Electronics and Telecommunication Engineering Bangladesh The University of Texas at Dallas Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350388282

This paper presents a performance comparison of a comb-shaped Vivaldi antenna for ultrawideband (UWB) applications, which is antipodal in nature. The antenna utilizes a 50 Ω microstrip feed line for energizing the antenna. To achieve ultrawide-band behavior, a parasitic elliptical-shaped patch and corrugated slots are employed. The patch controls frequency shifting along with high-gain behavior via electromagnetic coupling. To validate the proposed antenna, Rogers RT5880 substrate is used ( =2.2., tan .δ=0.0009). The proposed antenna operates in a frequency range from 5 to 28 G H z , with the highest gain of 12.5dBi at 27.5GHz. It has a total radiation efficiency of 97\% at 21.25GHz. The proposed Vivaldi antenna is a suitable candidate for high-gain wireless applications. The CST Studio Suite is employed to extract the simulation results of the antenna. © 2024 IEEE.

关键词： Microstrip antennas

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AI-Enhanced Farming: A Real-Time Monitoring App powered by IoT 5

AI-Enhanced Farming: A Real-Time Monitoring App powered by I...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Gayathri, R. Raaj, R. Sathesh Balaanand, M. Kumar, J. Arun Sasikala, N. Rajesh, K. Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Tagore Institute of Engineering & Technology Department of Computer Science and Engineering Salem India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Harohalli India Ssm Institute of Science and Technology Department of Electronics and Communication Engineering Dindigul India

ISBN: (纸本)9798331504403

The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. Despite various methods and solution evolving, still farmers can't upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost important tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over a system or smart phone. Moreover, the proposed approach can promote crop diversity and rotation for healthier soil, encourage sustainable practices like organic farming. © 2024 IEEE.

关键词： Soil moisture

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Intelligent IoT Based Temperature and Humidity Monitoring System for Tobacco Curing Barn 5

Intelligent IoT Based Temperature and Humidity Monitoring Sy...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Chundawat, Dipendra Pal Singh Mary, Gerardine Immaculate Julian, Anitha School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Saveetha Engineering College Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331518097

A Cloud-based real-time temperature and humidity monitoring device that monitors the tobacco curing process, automates the alarm, notifies in case of any deviation from prescribed levels of temperature and humidity, transmits temperature and humidity data to the cloud, and compares the data against the specified ranges. This system predicts the future values of temperature and humidity so one can calculate the efficiency of the system. The system tackles the issue of the Barn Monitoring System using a low-cost controller. The main idea behind the Intelligent IoT-based temperature and humidity monitoring system is to maintain the temperature and humidity inside the barn in each stage of curing. The device has a sensor for temperature and humidity. If the humidity crosses the threshold value, it can alert the barn driver and farmer via a Buzzer, SMS, or e-mail. © 2024 IEEE.

关键词： Tobacco

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Scalable Modular Battery Management System Using Model-Based Approach for EV Applications 2

Scalable Modular Battery Management System Using Model-Based...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Kumar, Srinath Jawaharram, Vishnu Ram Balashivakumaran, R. Chandrakala, K.R.M. Vijaya Kumar, S. Sampath Nadar, Kannan Paulish Amrita School of Engineering Department of Electrical and Electronics Engineering Coimbatore India Institute of Technology Jijiga University Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350328202

The global automotive industry is in the phase where Internal Combustion vehicles are in decline and witnessing a shift towards sustainable development. The major parameter of a successful EV is an efficient battery pack and the protection for it which is the Battery management system (BMS). In this work model-based approach in the design of a Modular Battery Management System (BMS) takes into account various protection schemes such as over and under-voltage scenarios adopted in Li-ion batteries and monitoring the State of Charge (SoC) using the Coulomb Counting technique. The design and the simulation-based approach are adopted to study the behaviour of Li-ion Battery and BMS in a wide operational condition where the testing is difficult using MATLAB/Simulink. The proposed technique is a Modular Battery Management System that works on the Master-Slave Architecture. © 2024 IEEE.

关键词： Battery management systems

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