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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Procedia Computer science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

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Deep Crowd Analysis to Spot Social Distancing Violations in Post-COVID 19 Lifestyle

Deep Crowd Analysis to Spot Social Distancing Violations in ...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： S. Sankara Narayanan A. Nesarani R. S. Raghav A. Robert Singh Suganya Athisayamani Department of CSE VelTech Rangarajan Dr.Sahunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vadeeswaram Guntur Andra Pradesh India School of Computing Sastra Deemed to be University Thanjavur Tamil Nadu India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India

Crowd analysis is a new field of study that involves processing a large group of people to examine one or more of their behaviors. Deep learning is an appropriate technique for crowd analysis using a convolutional neural network. To calculate the distance between crowd members and to identify social distance violations, a deep crowd analysis is proposed in this study. Pre-trained in a single class To discover the region of interest, CNN is utilised to classify people (RoI). The people in the picture are then localized using a density map. The reference point used to calculate the distance between the people is the centroid of the isolated areas in the density map. A social distance violation is reported if the estimated distance is less than the specified threshold distance (3 meters). Between the two ROIs, a distance measured in pixels is determined.

关键词： Deep learning Meters Technological innovation Surveillance Human factors Social factors Behavioral sciences

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Deep Convolutional Neural Network Framework for Brain Tumor Classification using MRI Images

Deep Convolutional Neural Network Framework for Brain Tumor ...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： U. Sakthi K. Thangaraj A Tamizhselvi M.K. Kirubakaran Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai TN India Department of Information Technology Sona College of Technology Junction Main Road Salem TN India Department of Information Technology St. Joseph’s College of Engineering Chennai TN India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai TN India

Brain cancer is one of the high-risk diseases and increases the death rate in all countries, affecting both men and women. The early diagnosis and severity of brain cancer leads to better medical treatment and save people’s lives. The machine learning procedure has been applied for early detection and treatment of brain cancer in the biomedical field by classifying them into low-risk and high-risk groups. In cancer research, the predictive and classification model has been developed using Deep Convolutional Neural Network (DCNN) algorithms for accurate decision making. The Magnetic Resonance Image (MRI) classification technique DCNN is advanced to detect and match feature points of training and test images. The DCNN classifier based on the outcome of feature points then classifies images. The key notion of this proposed research effort is to implement and execute the proposed DCNN algorithm on cancer patient datasets for risk level classification. The brain cancer affected patient details are collected from UCI machine learning data repository for experimental analysis. In this research study, the DCNN algorithm is proposed and it gives better accuracy and faster than the KNN, CNN and SVM.

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Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing

arXiv

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arXiv 2023年

作者： Lu, Wei Lee, Nic A. Buehler, Markus J. Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States MIT Media Lab Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties. © 2023, CC BY-NC-ND.

关键词： 3D printing

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K-net+segan-based segmentation with gannet aquila optimization algorithm-enabled deep maxout network for brain tumor classification using mri

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Journal of Mechanics in Medicine and Biology 2023年第5期23卷

作者： Ulaganathan, Sakthi Chen, Thomas M. Sathiyanarayanan, Mithileysh Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India Department of Electrical and Electronic Engineering School of Science and Technology City University of London LondonEC1V 0HB United Kingdom Research and Innovation MIT Square UK City University of London LondonEC1V 0HB United Kingdom

Segmentation and classification of brain tumor are time-consuming and challenging chore in clinical image processing. Magnetic Resonance Imaging (MRI) offers more information related to human soft tissues that assists in diagnosing brain tumor. Precise segmentation of the MRI images is vital to diagnose brain tumor by means of computer-Aided medical tools. Afterwards suitable segmentation of MRI brain tumor images, tumor classification is performed that is a hard chore owing to complications. Therefore, Gannet Aquila Optimization Algorithm_deep maxout network (GAOA_DMN) and GAOA_K-Net+speech enhancement generative adversarial network (GAOA_K-Net+Segan) is presented for classification and segmentation of brain tumor utilizing MRI images. Here, pre-processing phase performs noise removal from input image utilizing the Laplacian filter and also the region of interest (ROI) extraction is also carried out. Then, segmentation of brain tumor is conducted by K-Net+Segan, which is combined by Motyka similarity. However, K-Net+Segan for segmentation is trained by GAOA that is an amalgamation of Gannet Optimization Algorithm (GOA) and Aquila Optimizer (AO). From segmented image, features are extracted for performing classification phase. At last, brain tumor classification is conducted by DMN, which is tuned by GAOA and thus, output is obtained. Furthermore, GAOA_K-Net+Segan obtained better outcomes in terms of segmentation accuracy whereas devised GAOA_DMN achieved maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 92.7%, 94.5% and 91.5%. © 2023 World Scientific Publishing Company.

关键词： Generative adversarial networks

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Interface Faceting-Defaceting Mediated by Disconnections

arXiv

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arXiv 2022年

作者： Qiu, Caihao Salvalaglio, Marco Srolovitz, David J. Han, Jian Department of Materials Science and Engineering City University of Hong Kong Hong Kong Institute of Scientific Computing TU Dresden Dresden01062 Germany Dresden Center for Computational Materials Science TU Dresden Dresden01062 Germany Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong Shenzhen China

An intrinsic feature of nearly all internal interfaces in crystalline systems (homo- and hetero-phase) is the presence of disconnections (topological line defects constrained to the interface that have both step and dislocation character). Disconnections play a major role in determining interface thermodynamics and kinetics. We demonstrate that elastic interactions between disconnections lead to a thermodynamic, first-order, finite-temperature, faceting-defaceting transition, in agreement with experiments. These elastic interactions strongly modify equilibrium interface morphologies (compared with those solely determined by anisotropic surface energy) as well as the kinetics and morphologies of migrating interfaces. We demonstrate these phenomena through numerical simulations based upon a general, continuum disconnection-based model for interface thermodynamics and kinetics applied to embedded particles/grains, steady-state interface migration geometries, and nominally flat interfaces. Copyright © 2022, The Authors. All rights reserved.

关键词： Thermodynamics

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Vietnamese Scene Text Detection and Recognition using Deep Learning: An Empirical Study 6

Vietnamese Scene Text Detection and Recognition using Deep L...

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6th International Conference on Green Technology and Sustainable Development, GTSD 2022

作者： Pham, Nhat Truong Pham, Van Dung Nguyen-Van, Qui Nguyen, Bao Hung Minh Dang, Duc Ngoc Nguyen, Sy Dzung Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Deep Learning and Application Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Department of Telecommunications Engineering Ho Chi Minh City Viet Nam SK-Global JSC Ho Chi Minh City Viet Nam FPT University Computing Fundamental Department Ho Chi Minh City Viet Nam

ISBN: (数字)9781665466288

ISBN: (纸本)9781665466288

Scene text detection and recognition are vital challenging tasks in computer vision, which are to detect and recognize sequences of texts in natural scenes. Recently, researchers have investigated a lot of state-of-The-Art methods to improve the accuracy and efficiency of text detection and recognition. However, there has been little research on text detection and recognition in natural scenes in Vietnam. In this paper, a deep learning-based empirical investigation of Vietnamese scene text detection and recognition is presented. Firstly, four detection models including differentiable binarization network (DBN), pyramid mask text detector (PMTD), pixel aggregation network (PAN), and Fourier contour embedding network (FCEN), are employed to detect text regions from the images. Then, four text recognition models including convolutional recurrent neural network (CRNN), self-Attention text recognition network (SATRN), no-recurrence sequence-To-sequence text recognizer (NRTR), and RobustScanner (RS) are also investigated to recognize the texts. Moreover, data augmentation methods are also applied to enrich data for improving the accuracy and enhancing the performance of scene text detection and recognition. To validate the effectiveness of scene text detection and recognition models, the VinText dataset is employed for evaluation. Empirical results show that PMTD and SATRN achieve the highest scores among the others for text detection and recognition, respectively. For knowledge-sharing, our implementation is publicly available at. © 2022 IEEE.

关键词： Optical character recognition

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Cracking the quantum scaling limit with machine learned electron densities

arXiv

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arXiv 2022年

作者： Rackers, Joshua A. Tecot, Lucas Geiger, Mario Smidt, Tess E. Center For Computing Research Sandia National Laboratories Department Of Computer Science University Of California Los Angeles United States Institute Of Physics École Polytechnique Fédérale De Lausanne Computational Research Division Lawrence Berkeley National Laboratory Department Of Electrical Engineering And Computer Science Massachusetts Institute Of Technology United States

A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy. © 2022, CC BY.

关键词： Electrons

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Space-Frequency Diversity based MAC protocol for IEEE 802.11 ah networks 15

Space-Frequency Diversity based MAC protocol for IEEE 802.11...

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15th International Conference on Advanced Technologies for Communications, ATC 2022

作者： Minh Dang, Duc Ngoc Tran, Van Thau Nguyen, Hoang Lam Pham, Nhat Truong Tran, Anh Khoa Dang, Ngoc-Hanh Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Ton Duc Thang University Dept. of Electrical and Electronics Eng. Ho Chi Minh City Viet Nam Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Dept. of Telecommunications Engineering Ho Chi Minh City Viet Nam

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

IEEE 802.11ah is a sub-GHz communication technology to offer longer range and low power connectivity for the Internet of Things (IoT) applications. A Restricted Access Window (RAW) is specified to decrease the collision probability. Stations are divided into groups and stations from each group attempt to access the channel by employing the Distributed Coordination Function during their assigned RAW slots. However, the network throughput is limited by a single channel MAC protocol. In this paper, Space-Frequency Diversity-based MAC protocol for the IEEE 802.11ah network (SF-MAC protocol) is proposed to allow stations of different sectors to transmit packets on different channels with the help of Forwarders. The proposed SF-MAC protocol improves the packet delivery ratio and aggregate throughput of the network. © 2022 IEEE.

关键词： Internet of things

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