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Multi-Class Brain Tumour Classification with CNN on MRI Scans

Multi-Class Brain Tumour Classification with CNN on MRI Scan...

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IEEE international conference on Signal Processing, Information, Communication & systems (SPICSCON)

作者： Nisarga Mridha Sofia Noor Rafa Md. Siamul Islam Shayon Nusrat Sultana K. M. Safin Kamal Ahmed Wasif Reza Mohammad Shamsul Arefin Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

The process of classifying brain tumors using MRI data is difficult to do accurately. The challenges and limitations of manual analysis in treatments can be overcome by implementing computer-Aided Diagnosis (CAD) systems for categorizing brain tumors. This study assesses the performance of five well-known Transfer Learning CNN architectures - VGG19, VGG16, GoogleNet, ResNet-50, and DenseNet-121 - using magnetic resonance imaging (MRI) scans. The main goal is to classify brain malignancies into four groups: no tumor, pituitary, meningioma, and glioma. The focus of this research for the classification task was on data preparation, training on preprocessed data, and comparing each model using evaluation metrics such as f1-score, accuracy, recall, and precision. Following preprocessing and training, the evaluation of the five CNN models revealed promising results for the VGG models, with both VGG19 and VGG16 achieving an accuracy score of 97%. This study indicates that among the base variants of Transfer Learning models, VGG19 and VGG16 are well-suited for classifying different types of brain tumors.

关键词： Training measurement Solid modeling Accuracy Magnetic resonance imaging Transfer learning Brain tumors Signal processing Brain modeling Testing

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DKNAS: A Practical Deep Keypoint Extraction Framework Based on Neural Architecture Search 39

DKNAS: A Practical Deep Keypoint Extraction Framework Based ...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Liu, Li Cai, Xing Li, Ge Li, Thomas H. Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peking Univ Adv Inst Informat Technol Beijing Peoples R China Peking Univ Informat Technol R&D Innovat Ctr Beijing Peoples R China

ISBN: (纸本)9781728196817

Keypoint extraction including both keypoint detection and description is a fundamental step in a wide range of geometric multimedia applications. In recent years, many learning-based approaches for keypoint extraction emerge and achieve promising results. However, they usually design network architectures empirically and lack of considerations about the comprehensive performance, which leads to limited applications. In this paper, we propose a practical framework based on Neural Architecture Search (NAS) technology, DKNAS, which can search architectures automatically and maintain efficiency and effectiveness, simultaneously. To the best of our knowledge, the proposed framework is the first NAS framework for keypoint extraction. The evaluation on HPatches dataset shows that our method achieves state-of-the-art results in the metrics of repeatability, localization error, homography accuracy and matching scores. Besides, our model is applied to a traditional Simultaneous Localization and Mapping (SLAM) system, ORB-SLAM2, to replace the handcrafted keypoints. Experimental results demonstrate that the system adopting our model outperforms ORB-SLAM2 and some other deep keypoints enhanced systems.

关键词： measurement Location awareness Simultaneous localization and mapping Automation Computational modeling computer architecture Network architecture

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Multi-Class Classification and Detection of Brain Tumor ROI: Yolov9-CNN

Multi-Class Classification and Detection of Brain Tumor ROI:...

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Intelligent Algorithms for Computational Intelligence systems (IACIS), international conference on

作者： Priyanshu Sharma Garima Jain Pratham Darshan Kumar Thakkar Sanat Jain Ajay Kumar Phulre Suneet Joshi School of Computer Science and Engineering VIT Bhopal University Sehore India School of CSE VIT Bhopal University Sehore India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The detection and classification of brain tumors through medical imaging play a critical role in the identification and management of medical conditions. This study focused on two primary objectives: first, to accurately identify the ROI within brain scans, enabling precise localization of tumors, and second, to classify the tumors into specific types—meningioma, pituitary, and glioma—based on the identified ROIs. To achieve this, this study proposes a YOLOv9 deep learning architecture with a transfer learning hybrid framework. Proposed method began by initializing the network with pre-trained weights from a general object detection task. This model achieved competitive accuracy metrics, with an average mean average precision (mAP) of 0.85 for all tumor classes and 0.949 specifically for meningioma brain tumors. With six different Yolo models, the proposed methodology shows enhanced performance in the metrics. This research adds to the expanding field of deep learning applications in medical image analysis, showcasing opportunities for enhancing brain tumor detection and classification through advanced computer vision techniques.

关键词： measurement Deep learning Neuroimaging computer vision Computational modeling Transfer learning Object detection Brain modeling Object recognition Medical diagnostic imaging

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Enhanced Vision:Hybrid Deep Learning Based Model for Image Captioning and Audio Synthesis

Enhanced Vision:Hybrid Deep Learning Based Model for Image C...

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international conference on Advanced Computing and Communication systems (ICACCS)

作者： J Jenisha C Priyadharsini Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Image processing for the visually impaired is crucial. It empowers individual with visual impairments to perform daily tasks independently, contributing to improved quality of life, education, and employment opportunities. This paper tackles the challenge of image captioning with limited data by combining deep learning and supervised techniques like CNN and LSTM. the combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network involves training a computer to comprehend visual information in an image and generate descriptive captions. The proposed model employs a multi-layered architecture, combining the strengths of CNN s in feature extraction from images with the sequential learning capabilities of LSTMs to generate coherent and contextually relevant captions. The model utilizes the Google Text to Speech (gTTS) library for text-to-speech conversion. The findings suggest that among the tested models, the CNN- LSTM model achieved the highest predicted BLEU score of 95% for an image caption. Evaluation of the proposed model demonstrates its effectiveness in generating captions that align closely with human descriptions.

关键词： measurement Adaptation models Visualization Computational modeling Predictive models Feature extraction Text to speech Convolutional neural networks Long short term memory Context modeling

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Enhancing Smart Home Automation with Arduino and Artificial Intelligence

Enhancing Smart Home Automation with Arduino and Artificial ...

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international conference on System modeling & Advancement in Research Trends (SMART)

作者： Nazia Ahmad Sajithunisa Hussain Rubina Liyakat Khan Suhail Javed Quraishi Anupam Singh Remya P. George Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Computer Applications MRIIRS Faridabad Haryana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

The current instigation aims to examine how AI and IoT work hand in hand to use smart home automation with a special focus on the application based on Arduino microcontrollers. The suggested system focuses on increasing comfort, convenience, and safety of the home spaces by applying AI algorithms. The system comprises a number of integrated sensors and actuators that gather real time data and the data is analyzed and decisions are made by an AI control module. The strategies of design considerations, implementation, and the assessment of the energy usage, comfort level for the user and the security aspects of the system are also provided in the study. Exploitation of the proposed experimental benchmark confirms potential advancement compared to traditional systems, including the viability of successful, comprehensive smart home platforms. Keywords: Smart Home Automation, Energy Efficiency, Internet of Things (10 T), Artificial Intelligence (AI), Arduino Uno

关键词： System testing Automation Microcontrollers Smart homes User interfaces Sensor systems Time measurement Security Artificial intelligence Intelligent sensors

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A measurement based Predictive Maintenance Algorithm for Rigid-Body Dynamical systems using Radial Basis Function Approximation and Information Theory

A Measurement based Predictive Maintenance Algorithm for Rig...

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international conference on System Theory, Control, and Computing (ICSTCC)

作者： Jan Ortmann Mathis Ortmann Department of Electrical Engineering and Computer Science University of Siegen Germany Department of Mathematics Justus Liebig University of Giessen Germany

This paper presents new viewpoints to solve predicative maintenance problems for arbitrarily rigid-body mechanical systems. Reliable predictions for changes in physical parameters are highly dependent on system model accuracy. Hence, costs for physical system modeling are expensive and time-consuming. To overcome this problem, we replaced the modeling process with a full measurement based procedure in conjunction with radial basis functions (RBF) approximation to create the system model. Subsequently, information theory is employed to generate a meaningful metric quantity that reports about the changes in physical parameters. A rigid-body mechanical system is used as an example to verify the presented theory.

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Learning-Based BBA modeling Approach with Multi-Method Fusion

Learning-Based BBA Modeling Approach with Multi-Method Fusio...

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international conference on Information Fusion

作者： Siyuan Li Deqiang Han Jean Dezert Yi Yang School of Automation Science and Engineering Xi’an Jiaotong University Xi’an Shaanxi China The French Aerospace Lab - ONERA Chemin de la Hunière Palaiseau France SKLSVMS School of Aerospace Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Dempster-Shafer evidence theory (DST) is a theoretical framework for uncertainty modeling and reasoning, with modeling the basic belief assignment (BBA) as one of its most crucial and challenging tasks. The prevailing BBA determination methods have their own pros and cons, and the joint use of them is expected to provide a better BBA. To realize an end-to-end BBA modeling without explicitly using various prevailing BBA modeling methods, a learning-based BBA modeling approach with multi-method fusion (LBMMF) is proposed in this paper. Deep learning is used to train a deep network which learns the mapping from the training samples to the comprehensive BBAs obtained by jointly using the prevailing BBA modeling methods as the generalized training labels. Given a test sample, the corresponding BBA can be obtained in an end-to-end manner, which is the output of the trained deep neural network. Experimental results show that to use the BBA obtained by our method can achieve better classification performance.

关键词： Training Learning systems Deep learning Evidence theory measurement uncertainty Artificial neural networks Cognition

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A Hierarchical Bayesian Process for Inverse RL in Partially-Controlled Environments 21

A Hierarchical Bayesian Process for Inverse RL in Partially-...

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21st international conference on Autonomous Agents and Multiagent systems, AAMAS 2022

作者： Bogert, Kenneth Doshi, Prashant Computer Science Dept. University of North Carolina AshevilleNC28804 United States THINC Lab University of Georgia AthensGA30602 United States

ISBN: (纸本)9781713854333

Robots learning from observations in the real world may encounter objects or agents in the environment, other than the expert giving the demonstration, that cause nuisance observations. These confounding elements are typically removed in fully-controlled environments such as virtual simulations or lab settings. When complete removal is impossible the nuisance observations must be filtered out. However, identifying the sources of observations when large amounts of observations are made is difficult. To address this, we present a hierarchical Bayesian process that models both the expert's and the confounding elements' observations thereby explicitly modeling the diverse observations a robot may receive. We extend an existing inverse reinforcement learning algorithm originally designed to work under partial occlusion of the expert to consider the diverse and noisy observations. In a simulated robotic produce-sorting domain containing both occlusion and confounding elements, we demonstrate the model's effectiveness. In particular, our technique outperforms several other comparative methods, second only to having perfect knowledge of the subject's trajectory. © 2022 international Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved

关键词： Reinforcement learning

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Accurate EEG-Based Emotion Recognition using LSTM and BiLSTM Networks

Accurate EEG-Based Emotion Recognition using LSTM and BiLSTM...

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IEEE international conference on Smart Instrumentation, measurement and Applications (ICSIMA)

作者： Mashkuri Yaacob Teddy Surya Gunawan Muhammad Izwan Fazry Abu Bakar Siti Hajar Yusoff Mira Kartiwi Nelidya Md Yusoff Electrical and Computer Eng. Department International Islamic University Malaysia Kuala Lumpur Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Artificial Intelligence Universiti Tekologi Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

Emotion recognition is crucial for advancing human-computer interaction and mental health diagnostics. Unlike speech or image-based methods, EEG provides a direct, non-manipulable measure of neural activity, offering a more reliable insight into genuine emotional states. This study addresses the challenge of accurately recognizing emotions from EEG signals by leveraging deep learning techniques. The primary objective is to develop an efficient emotion recognition system using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks. EEG data were preprocessed, and features were extracted using wavelet packet decomposition. The LSTM and BiLSTM models were then trained to classify emotions into binary and multiclass categories. Results demonstrated that the BiLSTM model achieved superior accuracy, with 91.78% for binary classification and 85.21% for multiclass classification. These findings highlight the potential of advanced deep learning models in enhancing emotion recognition accuracy, contributing significantly to fields such as affective computing and mental health monitoring.

关键词： Deep learning Emotion recognition Recurrent neural networks Accuracy Computational modeling Mental health Brain modeling

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Narit Hnoohom Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Nakhon Pathom Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems.

关键词： Deep learning Emotion recognition Accuracy Computational modeling Bidirectional long short term memory Mental health computer architecture Brain modeling Electroencephalography Recording

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