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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur 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 Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach 6

Enhancing Soft Skills in Autistic Children: The Next Generat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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FT-AED: benchmark dataset for early freeway traffic anomalous event detection 24

FT-AED: benchmark dataset for early freeway traffic anomalou...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Austin Coursey Junyi Ji Marcos Quinones-Grueiro William Barbour Yuhang Zhang Tyler Derr Gautam Biswas Daniel B. Work Institute for Software Integrated Systems and Department of Computer Science Vanderbilt University Institute for Software Integrated Systems and Department of Civil and Environmental Engineering Vanderbilt University Institute for Software Integrated Systems Department of Computer Science Vanderbilt University Institute for Software Integrated Systems and Department of Computer Science and Department of Civil and Environmental Engineering Vanderbilt University

ISBN: (纸本)9798331314385

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and mistakes from manual crash reporting records make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Tennessee Department of Transportation Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/ to facilitate future research.

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Leveraging Convolutional Neural Network and Explainable AI for Enhanced Pneumonia Detection 1

Leveraging Convolutional Neural Network and Explainable AI f...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Murugan, Ramalingam Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331520540

Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification, particularly in detecting pneumonia from chest X-ray images. Despite their high accuracy, CNNs face challenges in clinical adoption due to their lack of interpretability. Medical practitioners require a clear understanding of the decision-making process to trust AI-generated diagnoses. To address the aforementioned challenge, we propose pneumonia classification model incorporating CNN with a combination of the model and Grad-CAM, for enhanced interpretability. Grad-CAM visually outlines critical regions, influencing the prediction, so the clinician can easily confirm AI-based diagnoses with a more confident and credible approach. It is exclusively for pediatric pneumonia, an accurate and explainable machine for early interventions that can also work in resource-scarce regions. Our research uses a set of 5,863 pediatric chest X-ray images, divided into Pneumonia and Normal classes. The dataset was acquired from Guangzhou Women and Children's Medical Center and divided into three subsets: training, validation, and test. By improving the accuracy and transparency of the diagnosis, this model can help bridge the gap between AI and the real world. This can help facilitate quicker and more reliable pneumonia diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information Technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of Technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

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computers, Materials & Continua 2024年第4期79卷 779-799页

作者： Ananthakrishnan Balasundaram Ayesha Shaik Japmann Kaur Banga Aman Kumar Singh Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India

Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have beenidentified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions isessential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcingemission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrialsmoke plumes using freely accessible geo-satellite imagery. The existing systemhas so many lagging factors such aslimitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timelyresponse to industrial fires. In this work, the utilization of grayscale images is done instead of traditional colorimages for smoke plume detection. The dataset was trained through a ResNet-50 model for classification and aU-Net model for segmentation. The dataset consists of images gathered by European Space Agency’s Sentinel-2 satellite constellation from a selection of industrial sites. The acquired images predominantly capture scenesof industrial locations, some of which exhibit active smoke plume emissions. The performance of the abovementionedtechniques and models is represented by their accuracy and IOU (Intersection-over-Union) *** images are first trained on the basic RGB images where their respective classification using the ResNet-50model results in an accuracy of 94.4% and segmentation using the U-Net Model with an IOU metric of 0.5 andaccuracy of 94% which leads to the detection of exact patches where the smoke plume has occurred. This work hastrained the classification model on grayscale images achieving a good increase in accuracy of 96.4%.

关键词： Smoke plume ResNet-50 U-Net geo satellite images early warning global monitoring

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Xtractor: A Two-Step Tweet Extractor for Sentiment Analysis 5

Xtractor: A Two-Step Tweet Extractor for Sentiment Analysis

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Alvi, Muhammad Bux Mahoto, Naeem Ahmed Unar, Mukhtiar Ali Shaikh, Muhammad Akram The Islamia University of Bahawalpur Computer Systems Engineering Bahawalpur Pakistan Mehran Uet Software Engineering Department Jamshoro Pakistan Mehran Uet Computer Systems Engineering Department Jamshoro Pakistan Pastic Pakistan Science Foundation Islamabad Pakistan

ISBN: (纸本)9798350394764

Twitter (now X) has been gaining popularity with each passing day since its inception in 2006. People have been using Twitter as an instant repository to collect data and gain insight into folks' minds on trending issues. Although Twitter allows access to its data through streaming and rest APIs, extracting the required data is difficult. The data (tweet) returned by Twitter is in json format, having at least 26 fields. Each field is bundled in the dictionary form data structure. tweet metadata such as tweet 'likes' and 'retweets' increase the volume and complexity of a tweet, making data extraction in a cleaner format more difficult. This work aims to develop an effective two-step tweet extractor (Xtractor: an online-offline keyword-based Twitter data extractor). In the first step, Xtractor collects publicly available topical tweets using keywords, hashtags, or their list on the fly, parses all the tweet fields, and filters them to retain potential field contents without additional payload. The second step of the Xtractor evicts partially matching tweets using the regular expression method to acquire the targeted domain-specific tweets. Using the proposed two-step Twitter data extraction method, datasets concerning the people of Pakistan have been collected, which can be leveraged to get insight for decision-making, specially in the context of sentiment analysis. We found this method efficient, capacitive, and productive. © 2024 IEEE.

关键词： Python

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of Information Technology (Singapore) 2024年 1-18页

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification 27

Siamese Multiple Attention Temporal Convolution Networks for...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 IEEE.

关键词： Convolution

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