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Improving Medical Imaging Diagnostics with Deep Convolutional Networks for Early Detection and Treatment

Improving Medical Imaging Diagnostics with Deep Convolutiona...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Krishnakumar, K. Gurrapu, Omprakash Reshma, P. Krishnammal, P. Muthu Khan, Huma Qamar Amane, Ninad Saveetha School of Engineering Chennai India Volvo Trucks North America NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India VIT-AP University Amaravati India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Bms Institute of Technology and Management Department of Ai&ml Bangalore India

ISBN: (纸本)9798331538538

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical information. In this paper, we reveal the potential of deep convolutional networks (DCNs) in the field of medical image diagnostic. It means that, with the help of deep learning algorithms DCNs can learn meaningful features of medical images and improve diagnostic accuracy and speed. The near proposed technique involves the use of deep convolutional network model to analyse medical images for diagnosis of diseases such as cancer, heart diseases and neurological disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and treatment is established. © 2025 IEEE.

关键词： Neuroimaging

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Performance Analysis of Machine Learning Classifiers for Metastatic Breast Cancer Diagnosis Within 90 Days

Performance Analysis of Machine Learning Classifiers for Met...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Vaishnavi Chopade Pritam Tambe Sayali Satokar Anuradha D. Thakare Manisha Mantri Department of Computer Science & Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune Pune India Artificial Intelligence and Quantum Group at C-DAC Centre for Development of Advanced Computing Pune India HPC-Medical and Bioinformatics Group at C-DAC Centre for Development of Advanced Computing Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Metastatic breast cancer, commonly referred to as stage IV breast cancer or advanced breast cancer, is a severe form of the disease, occurs when breast cancer cells migrate to different parts of the body such as the lungs, bones, liver and brain. This study focuses on predicting the of metastatic breast cancer diagnosis within a 90-day window. The dataset was meticulously processed, addressing missing values and encoding categorical variables to ensure a more structured and interpretable dataset. Visualizations were utilized to explore insights and relationships within the data. The results of different machine learning algorithms and deep learning model were evaluated, including Naive Bayes, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and TabNet. Our findings reveal that GBM and TabNet outperformed other algorithms with respect to performance, achieving accuracies of 81.68% and 81.64%, respectively. Furthermore, each algorithm's performance was evaluated using the AUC-ROC curve.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Prediction algorithms Boosting Breast cancer Classification algorithms Decision trees Random forests

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Detection and Classification of Arrhythmia Using Hybrid Deep Learning Model

Detection and Classification of Arrhythmia Using Hybrid Deep...

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Next Generation Electronics (NEleX), International Conference on

作者： Trinaya Kodavati M Rithani K Venkatraman SyamDev R S Department of Computer Science & Engineering Amrita School of Computing Chennai India Department of AI-ML New Horizon College of Engineering Bangalore India

Cardiovascular disorders, encompassing arrhythmias rhythms, represent noteworthy worldwide health issues that necessitate prompt identification and precise categorization to facilitate efficacious therapeutic measures. This study examines the status of the field's research at the moment, going into cardiac arrhythmias' physiological foundation, clinical ramifications, and diagnostic resources. The paper proposes a methodology that combines MobileNet V1 with Gated Recurrent Unit (GRU) for the detection and classification of Arrhythmia, which has achieved an accuracy of 99.4%, recall score of 0.98 and precision score of 0.99. The MIT-BIH Arrhythmia dataset, consisting of recordings by the BIH Arrhythmia Laboratory, is utilized for training and validation of the model. The findings demonstrate the potential of using the MobileNetV1+GRU model for detection and classification of different types of Arrhythmia, which can be applied for diagnosis in hospitals, clinics, emergency departments and screening programs.

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SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique

SE-Resnet152 Model: Early Corn Leaf Disease Identification a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kalpana, Ponugoti Chanti, Yerrolla Ravi, G. Regan, D. Pareek, Piyush Kumar Avn Institute of Engineering & Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Sona College of Technology Dept of Ece Salem India Siddartha Institute of Science and Technology Department of Ece Puttur India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Various corn leaf diseases reduce the quantity and quality of corn crop production, so early detection and classification are important for preventing crop yield. However, the detection and classification of corn leaf disease are more difficult due to the regions of the leaf blur and noise effect. To solve the above-mentioned problems, a feature-based transfer learning approach called the Convolutional Deep Learning (CDL) model is proposed. First, the images of corn leaves taken from Plant Village are preprocessed with Green Channel Conversion (GCC) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to enhance low-contrast images. Then the important features are extracted through ResNet152, which contains two fully connected layers with a sigmoid function. Next, squeeze and excitation are combined with ResNet152 to gain better performance in detection and classification. The proposed SE-ResNet152 model outperforms with a Precision 98.71%, Recall 98.14%, F1-score 96.87% and accuracy 99.02% respectively compared to existing model like U-Net and ResNet50 models. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Green Channel Extraction Low-Contrast Images ResNet152

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Wireless Capsule Endoscopy Bleeding Images Classification using CNN Based Model

Wireless Capsule Endoscopy Bleeding Images Classification us...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Gotte Ranjith Jeyasudha, J. Ravi, G. Jagadesh, T. Madhura, G.K. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Insitute of Science and Technology Department of Computational Intelligence Chennai India Sona College of Technology Dept of Ece Salem India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Wireless Capsule Endoscopy (WCE) is an effective utensil with probe alimentary region diseases and executes effortless visualizing in intestine. Numerous enterprises produce its broad connection and adaptation challenging things comparable efficacity, forbearance, immunity, and consummation. Self-regulated study of the WCE handed datafile is of immense significance for discriminating peculiarity. Detecting a case's gastrointestinal region through WCE outcomes a huge datafile that involves a significant quantum moment with an exceptional competence put from the therapeutic knowledge. So many computers-backed, eyesight-grounded results can be recommended to determine this controversy, so far it don't give asked position in delicacy, farther advancements just the same as demanded. Combine with deep neural network to develop a proposed model called BIR (bleedy image recognizer) to classify images of bleeding detected by a WCE. The BIR model is combination of MobileNet and a custom-built BERT model. BIR utilizes the MobileNet model for original-position calculation for its lesser calculation energy demand and latterly the affair is fed to the BERT model for farther processing. utilized a datafiles consisting of 1650 pictures captured by WCE for the purpose of training and testing the BIR mode. © 2023 IEEE.

关键词： BERT models Bleedy image recognizer computer vision deep learning gastrointestinal tract infection Wireless capsule endoscopy

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Improved Multiview Graph Convolutional Network for Threat Detection in Internet of Things-based Smart Farming Systems

Improved Multiview Graph Convolutional Network for Threat De...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： S. P. Prakash Haydeer MohamadAbbas Meghana A Adam Smith G K. Ghamya Department of Electronics and Communication Engineering Bannari Amman Institute of Technology India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Civil Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Over past few years, the agriculture industry has experienced a significant transformation in farming by adapting advance technologies like Internet of Things (IoT), which leads to a modern era of smart farming systems. These systems use smart technologies such as IoT devices and sensors for optimize farming. However, these systems possess various cyber threats such as data breaches, device tampering and unauthorized access which complicates the system functionality. Therefore, this research proposes Improved Multiview Graph Convolutional Network (IMGCN) for threat detection. Initially, data is collected from Neuro Fuzzy Bot (NF-Bot-IoT) and preprocessed by using Synthetic Minority Over-sampling Technique (SMOTE) which handles class imbalance issues. Further, feature selection is performed using Recursive Feature Elimination (RFE) which reduces the impact of noisy or irrelevant features. Finally, threat detection in smart farming systems is done by using proposed IMGCN which detects threats and protects data. The proposed IMGCN achieved better results in terms of accuracy (93.25%), precision (92.23%) as well as F1-Score (91.78%) when compared with existing Multi-Layer Perceptron (mlP).

关键词： Smart agriculture Performance evaluation Accuracy Graph convolutional networks Telecommunication traffic Feature extraction Threat assessment Sensor systems Internet of Things Farming

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Sustainable Practices in Enhanced Diagnosis and Monitoring of Polycystic Ovary Syndrome (PCOS) using Machine Learning

Sustainable Practices in Enhanced Diagnosis and Monitoring o...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Channabasamma Dharmapuri Siri Macha Harshitha Kammagani Shobhan Babu Aleem Mohammad Ahmad Alkhayyat Computer science & Engineering Gokaraju Rangaraju institute of Engineering And Technology Telangana India Computer science & Engineering (AI&ML) Gokaraju Rangaraju Institute of Engineering and Technology Telangana India Electronics & Communication Engineering K.G Reddy College of Engineering and Technology Hyderabad Telangana India Computers Techniques engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting a significant percentage of women of reproductive age, leading to various metabolic, reproductive, and psychological issues. Despite its prevalence, the diagnosis and monitoring of PCOS remain challenging due to its heterogeneous nature and the multifactorial aspects of its manifestation. This study presents the development and implementation of advanced machine learning techniques aimed at enhancing the diagnosis and monitoring of PCOS, leveraging predictive analytics to improve medical outcomes. Introduced a comprehensive framework that integrates diverse data sources, including clinical, hormonal, and ultrasound data, along with patient lifestyle and genetic information. Advanced machine learning models including Random Forest, Decision Trees, and Logistic Regression were employed and compared to identify the most effective approach for PCOS diagnosis and prognosis. The models were trained, validated, and tested on a large dataset obtained from various healthcare facilities, ensuring a robust and generalizable system. The results demonstrate the potential of machine learning in revolutionizing the management of PCOS. The models achieved high accuracy, sensitivity, and specificity in diagnosing PCOS and predicting associated complications, such as infertility, metabolic syndrome, and cardiovascular risks. Furthermore, the system provides personalized treatment and lifestyle recommendations, contributing to a holistic approach to managing PCOS.

关键词： Technological innovation Ultrasonic imaging Soft sensors Machine learning Medical services Sensitivity and specificity Predictive analytics Monitoring Medical diagnostic imaging Regression tree analysis

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Circle Search Algorithm with Convolutional Neural Network for Distributed Denial-of-Service Attack Detection in Software-Defined Networks

Circle Search Algorithm with Convolutional Neural Network fo...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： S. Padmakala Hassan M. Al-Jawahry R. Palanivel Y.M. Mahaboob John Arelli Madhavi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE Mahendra College of Engineering Salem India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The Distributed Denial-of-Service (DDoS) attack is a crucial cybersecurity threat for Software-Defined Network (SDN). However, DDoS in SDN severely affect network availability which reduces the classifier performance. Hence, Circle Search Algorithm with Convolutional Neural Network (CSA-CNN) is proposed for detecting DDoS anomaly attacks in SDN. The DDoS anomaly dataset such as CIC-DDoS2019 is preprocessed through label encoding which changes categorical data to arithmetical data types through allocating single numerical labels to every class. Then, CSA is applied for feature selection which helps to efficiently detect malicious traffic patterns by exploring local and global search spaces and improves the detection accuracy. The CNN has ability to detect known and unseen attacks due to handling multidimensional data which increase the security of SDN. The CSA-CNN achieved better performance in terms of 99.97%, 99.95%, 99.94% and 99.98% of precision, sensitivity, f1-score and accuracy for CIC-DDoS2019 dataset when compared to Gated Recurrent Unit (GRU).

关键词： Accuracy Sensitivity Traffic control Network security Feature extraction Encoding Vectors Convolutional neural networks computer crime Software defined networking

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An Upscaled Framework for Emotional Reponse System: A Support Solution from ai

An Upscaled Framework for Emotional Reponse System: A Suppor...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Srimathi M D. Muruganandhan Aurobind. G Dept. of Computer Science (AI&ML) Rajiv Gandhi National Institute of Youth Development Sriperumbudur T.N. India Department of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Pondicherry India Department of Computer Science (Data Science) Rajiv Gandhi National Institute of Youth Development Sriperumbudur T.N. India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Virtual assistants have become essential in simplifying tasks in our busy lives. These advancements in artificial intelligence could become even more impactful by incorporating voice emotion recognition, enabling more natural, personalized interactions without constant typing. In this research, we introduce KANMANI, a model built on Machine Learning, Deep Learning, and ai to enhance virtual assistance by facilitating voice and video interactions with emotion and speech recognition. This responsive design is invaluable for users seeking connection even when alone. KANMANI's core objective is to “Empower minds and ignite possibilities by unlocking the future through personalized technology.” Structurally, KANMANI integrates three key components, each tailored for an intuitive user experience. The first, the Voice Assistant, utilizes ai and Natural Language Processing (NLP) to interpret spoken commands, creating seamless interaction. The second, Emotion Detection, identifies user emotions via speech and facial expressions, responding appropriately-such as with an encouraging quote. The final component, the Virtual Assistant, enables video-based interaction with real-time emotion detection, delivering responses that reflect empathy and awareness. Together, these features make KANMANI a customized virtual companion that supports and adapts to the user's world, offering practical assistance and emotional connection.

关键词： Productivity Emotion recognition Avatars Personal voice assistants Mental health Streaming media Real-time systems User experience Natural language processing Artificial intelligence

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Design and Evaluation of IoT Prototypes: Leveraging Test-Beds for Performance Assessment and Innovation

Design and Evaluation of IoT Prototypes: Leveraging Test-Bed...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Arnav Kotiyal Anurag Shrivastava Amandeep Nagpal Manjunatha Krishna Kant Dixit R. Akhilesh Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Lovely Professional University Phagwara India Department of Mechanical Engineering New Horizon College of Engineering Bangalore Department of Electrical Engineering GLA University Mathura India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

When you mention the Internet of things (IoT), connected devices and systems can be made seamless. Whilst creating and prototyping IoTs are oftentimes necessary, design, testing and evaluating IoT prototypes require robust frameworks and methodologies to achieve reliable performance and innovation. In this paper, test beds as controlled environments for performance assessment of IoT prototypes are discussed and these tested in terms of design and evaluation. Of importance are test-beds as they aid in simulating real world conditions to do iterative improvements, and foster innovation in IoT solutions. It presents some key methodologies for developing IoT prototypes, underlines the role of test beds in the validation process, and reports about evaluation of some case studies demonstrating the impact of such frameworks on alleviating inefficiency, unreliability, and non-scalability. The paper combines leading edge technologies with strong data driven decision making to present actionable feedback on exploiting test beds to catalyse IoT innovation against pivotal challenges including interoperability, security, and scalability.

关键词： Performance evaluation Technological innovation Scalability Prototypes Packet loss Reliability engineering Throughput Real-time systems Internet of Things Edge computing

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