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Alzheimer's Disease Classification using Capsule Network 4

Alzheimer's Disease Classification using Capsule Network

引用

4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Pattanayak, Binod Kumar Mansingh, Padmini Pati, Bibudhendu Dash, Bibhuti Bhusan Gourisaria, Mahendra Kumar Patra, Sudhansu Shekhar ITER SOA Deemed to be University Department of Computer Science and Engineering Bhubaneswar India Rama Devi Women's University Department of Computer Science Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9798350385793

The most prevalent type of dementia is Alzheimer's disease, which impairs thinking, memory, and communication abilities. Better patient treatment is made possible with early disease identification. To detect and predict Alzheimer's disease through the analysis of Magnetic Resonance Images (MRIs), deep learning and big data analytics have recently been created. To improve the classification of Alzheimer's disease patients, this work utilizes capsule network. A critical evaluation is carried out on patients with Alzheimer's disease to improve stage-specific classification. Specifically, the study analyses various methods for performing abnormality identification in brain MRI images and pinpoint the shortcomings of those methods. Thus, it is possible to close the research gaps and enhance the shortcomings of different approaches to produce more accurate and superior results. © 2024 IEEE.

关键词： Convolutional neural networks

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

引用

2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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Ground Robot Detection for Collaborative UAV-UGV Alignment and Retrieval Operations 15

Ground Robot Detection for Collaborative UAV-UGV Alignment a...

引用

15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Pepito, Jamaica Mae L. Aleluya, Earl Ryan M. Bolaybolay, John Mel A. Alagon, Francis Jann A. Clar, Steve E. Pao, Jeanette C. Salaan, Carl John O. Guirnaldo, Sherwin A. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Volcanologists face considerable challenges in ex-ploring and monitoring harsh and dangerous environments like volcanoes. To gather essential data, they use robots, such as drones and mobile robots. However, drones have limited capacity and flight time, while mobile robots face mobility constraints. Combining drones and mobile robots has emerged as an inno-vative technology for long-range monitoring of volcanoes. This approach allows for fast deployment of ground robots using transport drones. However, the retrieval operation of the ground robot is challenging due to inaccurate drone positioning. A visual technique for detecting and aligning the ground robot is crucial during this procedure. In this study, the detection of the ground robot utilizing a drone was investigated. The drone-based detection approach involved (i) capturing ground robot images from various conditions, (ii) training a robust detection model, and (iii) assessing its performance. Furthermore, this study employed the You Only Look Once (YOLOv8) architecture for ground robot detection. Three models were investigated: the plate detection model, the ground robot detection model, and the model for both ground robot and plate detection. The trained ground robot detection model achieved a robust detection performance garnering the precision of 98.9% with a respectable recall of 100%, F1 score of 99.94%, and 98.6% mean average precision, compared to the other two trained models. The trained detection model presented a practical approach, which produced good performance and could predict and segment the ground robot during the alignment procedure for the retrieval operation by the drone. © 2023 IEEE.

关键词： Drones

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Plant Leaf Diseases: Feature Extraction and Identification Using Convolutional Neural Networks and GLCM features

Plant Leaf Diseases: Feature Extraction and Identification U...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Mohanty, Arpeeta KIIT Deemed to Be University Bhubaneswar School of Computer Applications India KIIT Deemed to Be University Bhubaneswar School of Law India KIIT Deemed to Be University Bhubaneswar School of Computer Engineering India

ISBN: (纸本)9798350369175

The economics of a nation is greatly impacted by agricultural production, and identifying diseases of plant leaves is essential to preserving yield quantity and quality. This study demonstrates a novel deep convolutional neural networks (DCNN) technique for detecting plant leaf disease. Plant leaf photos are first preprocessed using AMA (Adaptive Mean Adjustment) method to improve them and used a 2D Adaptive Anisotropic Diffusion Filter (hereafter 2D AADF) to remove noise. An improved method of segmentation is to use Adaptive Otsu (AO) thresholding along with Enhanced Fast Fuzzy C Means Clustering (hereafter EFFCMC). After extracting Grey Level Co-occurrence Matrix (GLCM) features from segmented pictures, Principal Component Analysis (PCA) is used to reduce the dimensionality. In experiments, the new DCNN architecture-which consists of two fully linked layers, a SoftMax layer, and four convolutional layers-achieves better classification results. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning-Based Approach for Detecting Copy-Move Forgery in Digital Images 27

Deep Learning-Based Approach for Detecting Copy-Move Forgery...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Dar, Navid Ahmad Atsya, Rani Kumar, Anuj Sharda University dept. of Computer Science and Engineering UP Greater Noida India Greater Noida Institute of Technology dept. of computer applications UP Greater Noida India

ISBN: (纸本)9798350392319

Copying and pasting a portion of the same image to hide another portion is the most common method of fraudulent image enhancement. Cloning a restricted portion of an image and pasting it once or more inside a comparable picture is known as copy-move image forgery (CMF). The procedure aims to highlight or hide a certain aspect in the finished picture, usually a person or an object. The harm brought about by the horrible deed frequently takes unexpected forms. For the automated identification of CMF, the paper proposes a Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) approach. The CASIA 2.0 dataset's, which includes both real and fake photos, was used in the study. The proposed model is simple to use, and its application shows that it is faster than previous approaches that are now regarded as state of the art. The results show that, when compared to the other currently available methods, the recommended approach achieves the highest level of accuracy (90.56%). © 2024 IEEE.

关键词： Generative adversarial networks

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Deep Learning for Alzheimer's Diagnosis: ResNet152V2 Approach on MRI Dataset 1

Deep Learning for Alzheimer's Diagnosis: ResNet152V2 Approac...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Adjei, Peprah Obed Pattnayak, Parthasarathi Mohanty, Arpeeta Das, Tulip Patnaik, Sanghamitra Ansah, Patrick Manneh, Madlyn School of Computer Applications KIIT Deemed to be University Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India SRI SRI University Faculty of Emerging Technologies Cuttack India School of Law KIIT Deemed to be University Bhubaneswar India School of Computer Applications IIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9798350361780

Alzheimer's Disease (hereafter AD), a progressive neurodegenerative disorder, poses a significant global health challenge. This research presents a convolutional neural network (CNN)-based algorithm utilizing the ResNet152V2 architecture to classify AD severity from MRI images. Our method makes use of machine learning to reliably identify the various stages of AD, allowing for an early and accurate diagnosis. The ResNet152V2 architecture demonstrates exceptional performance, achieving a test accuracy of 97.31%. The model's robustness is validated through comprehensive evaluations, including precision, recall, and f1-score This research contributes to the field by providing a reliable and effective tool for the automated detection of AD severity, potentially aiding clinicians in timely intervention and treatment planning. The utilization of augmented MRI data enhances the model's generalization capability, making it adaptable to diverse clinical scenarios. © 2024 IEEE.

关键词： Image classification

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Eggplant Leaf Semantic Segmentation in Aerial Imagery for Precision Agriculture 15

Eggplant Leaf Semantic Segmentation in Aerial Imagery for Pr...

引用

15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Usman, Nouran M. Aleluya, Earl Ryan M. Clar, Steve E. Alagon, Francis Jann A. Salaan, Carl John O. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Eggplant is one of the prominent vegetable crops in the Philippines. Traditional farming practices, like agrochemical spraying, often lack real-time, high-resolution data crucial for informed decisions. Monitoring vegetation indices is vital for crop growth assessment, health monitoring, and precision agriculture. However, optimizing agrochemical spraying for specific crop needs remains a challenge. Unmanned aerial vehicles (UAV s) offer a transformative solution. RGB cameras, designed for visual imagery, may not provide accurate vegetation index measurements. computer vision, through leaf detection, plays a crucial role in assessing crop health. This study aims to distinguish eggplant leaves from their surroundings. Subsequently, the authors compared two open-source segmentation models, namely U-Net and FPN. Two pre-trained encoders, VGG16 and VGG19, were used in both models to further evaluate their performance. Using a drone-mounted RGB camera, the authors gathered images of mature eggplant leaves. The experimental findings demonstrate the effectiveness of the VGGl6-based FPN architecture for real-time leaf segmentation tasks as it acquires 97.24 % and 96.73 % accuracy with a latency time of 31.5 ms and 32.6 ms, on 0.5 and 0.75 threshold values. The study recommends FPN with VGG16 for its accurate and low-latency performance in time-sensitive leaf segmentation applications. © 2023 IEEE.

关键词： Antennas

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Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach 5

Retinal Scan Denoising Using Generative Adversarial Networks...

引用

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

作者： Gowthami, G. Mano Shankari, J. Babu, Tina St. Francis de Sales College Department of Computer Applications Bengaluru India Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798331518097

Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. These methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. This paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images. © 2024 IEEE.

关键词： Generative adversarial networks

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An Elaborate Review of Diagnosing Glaucoma Disease Using Machine Learning and Deep Learning Techniques

An Elaborate Review of Diagnosing Glaucoma Disease Using Mac...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Thamilselvan, R. Natesan, P. Rajalaxmi, R.R. Mohan, S. Muruganantham, S. Vanitha, R. Kongu Engineering College Perundurai Erode638060 India Kongu Engineering College Department of Computer Applications Perundurai638 060 India

ISBN: (纸本)9798350348217

The vision of a human is very important in their life. In today's world lot of medical advancements have arrived to treat all the diseases. In this part the earlier identification of the disease is still a challenging part. In this review the study path mainly focus on the eye disease. The major vision problem with eye sight is due to cataract and glaucoma. It is curable if we take utmost care in the early stage of cause by constant checkup. Glaucoma is an optical problem caused by the neuropathy due to loss of retinal ganglion cells. This Glaucoma can be detected through fundus images, which will help to identify the vision loss at the earlier stage. Optic Nerve Head (ONH) was the first parts that get damaged by the glaucoma so its diagnosis will always focus on the parts such as Optic cup and Optic Disc in the retina. In this review work the datasets based on these were collected and study was made based on the various machine learning and deep learning techniques. © 2023 IEEE.

关键词： Eye protection

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An Ensembled Grid based Machine Learning Approach For PD Classification From MRI Images 4

An Ensembled Grid based Machine Learning Approach For PD Cla...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Redhya, M. Jayalakshmi, M. Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350343670

Parkinson's disease (PD) is a neuro-degenerative disease caused due to breakdown of brain cells in the central-part of the nervous system. As symptoms of PD appear only after 60% or more of these cells are destroyed, early detection is a tedious task. Several studies have proven that PD can be effectively diagnosed from MR-Images (MRI). This paper provides an ensembled machine learning model, Stacked-EG model, for MRI-image based PD classification. The dataset was obtained from PPMI (Parkinson's Progression Markers Initiative) online repository which included 134 PD subjects (80 males and 54 females) and 126 healthy patients (60 males and 66 females) around 60 years old on average. Feature extraction is performed through the application of filters, including wavelet transformations and intensity-based enhancements. Feature selection is conducted using optimised XGBoost model's feature significance scores. For classification, a Stacking Classifier which combines the strengths of XGBoost, SVM, and a Random Forest classifier was used. The model obtained an accuracy of 98.41% in classifying PD from healthy controls. © 2024 IEEE.

关键词： GridSearch Optimization Magnetic Resonance Imaging (MRI) Random Forest Stacking SVM XGBoost

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