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Computational intelligence in eye disease diagnosis: a comparative study

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第3期61卷 593-615页

作者： Kumar, S. v. Mahesh Gunasundari, R. Amrita Coll Engn & Technol Dept Elect & Commun Engn Nagercoil Tamil Nadu India Puducherry Technol Univ Erstwhile Pondicherry Engn Coll Dept Elect & Commun Engn Pondicherry India

In recent years, eye disorders are an important health issue among older people. Generally, individuals with eye diseases are unaware of the gradual growth of symptoms. Therefore, routine eye examinations are required for early diagnosis. Usually, eye disorders are identified by an ophthalmologist via a slit-lamp investigation. Slit-lamp interpretations are inadequate due to the differences in the analytical skills of the ophthalmologist, inconsistency in eye disorder analysis, and record maintenance issues. Therefore, digital images of an eye and computational intelligence (CI)-based approaches are preferred as assistive methods for eye disease diagnosis. A comparative study of CI-based decision support models for eye disorder diagnosis is presented in this paper. The CI-based decision support systems used for eye abnormalities diagnosis were grouped as anterior and retinal eye abnormalities diagnostic systems, and numerous algorithms used for diagnosing the eye abnormalities were also briefed. various eye imaging modalities, pre-processing methods such as reflection removal, contrast enhancement, region of interest segmentation methods, and public eye image databases used for CI-based eye disease diagnosis system development were also discussed in this paper. In this comparative study, the reliability of various CI-based systems used for anterior eye and retinal disorder diagnosis was compared based on the precision, sensitivity, and specificity in eye disease diagnosis. The outcomes of the comparative analysis indicate that the CI-based anterior and retinal disease diagnosis systems attained significant prediction accuracy. Hence, these CI-based diagnosis systems can be used in clinics to reduce the burden on physicians, minimize fatigue-related misdetection, and take precise clinical decisions.

关键词： Human eye Anterior eye Retina Ophthalmology Eye diseases Digital imaging Segmentation Computational intelligence Classification

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Recent advances in automatic feature detection and classification of fruits including with a special emphasis on Watermelon (Citrillus lanatus): A review

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NEUROCOMPUTING 2023年第1期526卷 62-79页

作者： -Hernandez, Danilo Caceres Gutierrez, Ricardo Kung, Kelvin Rodriguez, Juan Lao, Oscar Contreras, Kenji Jo, Kang-Hyun Sanchez-Galan, Javier E. Univ Tecnol Panama Fac Ingn Electr Campus Victor Levi Sasso Panama City Panama Univ Tecnol Panama Fac Ingn Sistemas Computac Campus Victor Levi Sasso Panama City Panama Univ Ulsan Grad Sch Elect Engn Intelligent Syst Lab Address One Ulsan South Korea Secretaria Nacl Ciencia Tecnol & Innovac SENACYT Sistema Nacl Invest SNI Panama City Panama

This document provides an overview of advances in the task of automatic feature detection and classifi-cation of fruits, with and special interest in watermelon (Citrillus lanatus). The review was written with the objective to highlight the wealth of knowledge that exist in the application of analytical, smart and sensing image recognition techniques commonly used in agro-industry, and the computational approaches used to make the classification possible. Also, an specific interest is put in the contributions made in the development of automatic recognition systems geared towards for watermelon using images, acoustic and spectroscopy methodologies. The importance of this document is that it provides a conceptual summary of the methods for the automatic recognition of fruits, including machine learning and evolutionary computational algorithms to analyze their sensed data. In conclusion this is a first step into recognizing the challenges and opportunities that can be addressed in this field to augment the vis-ibility of these methods and to further modernize the agro-industrial sector.(c) 2023 Elsevier B.v. All rights reserved.

关键词： Agro-industry Fruits Watermelon Machine learning image processing Feature recognition

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Classification and Localization of Objects Using Faster RCNN 6th

Classification and Localization of Objects Using Faster RCNN

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6th International Conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Dakey, Bhavya Sree Ramani, G. Shabber, Md. Jogu, Sirisha Javvaji, Divya Sree Jangam, Hasini Department of CSE B V Raju Institute of Technology Narsapur Telangana Medak502313 India

ISBN: (纸本)9789819984503

On the basis of modern technological advancements, novel and sophisticated algorithms have been devised. The progression in object detection technologies, similar Fast and Faster RCNN algorithms, have resulted in reduced detection time of objects/entities, coupled with high precision levels. The latest study examines the efficiency of a newly proposed algorithm that detects RPN and Fast RCNN. The input ROIs for the RCNN network, using the RPN’s region proposals, a potential way to unify the components is by creating a single network that facilitates communication or transfer of convolutional features to recognize a specific object within each image. By employing a unified network, the requirement to obtain Region of Interest (ROI) from an external network is eliminated making the process cost-effective. The efficiency and accuracy of object detection have been crucial topics around the development of computer vision systems. With the foundation of deep learning techniques, there has been significant progress in the precision of object detection. The proposed project incorporates classification and localization for object detection. Precisely, the system takes an image as input and produces an output that consists of bounding boxes for each object present in the image, along with corresponding information about the class of each object contained within the respective bounding box. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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MedSeg: A Medical image Analyzer

MedSeg: A Medical Image Analyzer

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Control Instrumentation System Conference (CISCON) - Guiding Tomorrow - Emerging Trends in Control, Instrumentation, and systems Engineering

作者： Mehta, Shlok Pancholi, Keyur Pandey, Sakshi Dhage, Dr Sudhir Sardar Patel Inst Technol Comp Engn Dept Mumbai Maharashtra India

ISBN: (纸本)9798350375480;9798350375497

This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced image processing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception v3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.

关键词： Machine learning Medical image analysis Chest X-Rays prediction imageNET Inceptionv3 Disease prediction

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An Efficient ODR for Computer Night vision Using Multi-Scale Retinex Network-Aided Enhanced images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Charles Prabu, v. Pandiaraja, P. Sathiyamoorthi, v. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. Computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once version 7 (Yolov7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Algorithm of Palm vein image Selection for Biometric Authentication systems Based on Multidimensional Markov Chain 25

Algorithm of Palm Vein Image Selection for Biometric Authent...

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25th International Conference on Digital Signal processing and its Applications, DSPA 2023

作者： Kharina, Natalya Zemtsov, Anton Chernyadyev, Sergei Vyatka State University Department of the Radio Electronic Means Kirov Russia Institute of Mathematics and Information Systems Vyatka State University Kirov Russia

ISBN: (纸本)9798350337235

Biometric palm vein authentication becomes more popular in the industry. This paper proposes an algorithm for palm vein image selection. The main idea of this method is an approximation of biometric template image through discrete Markov process and applying the theory of conditional Markov process to develop the algorithm, which allows to fully utilize the statistic redundancy of the biometric template. Given method can be effectively used after performing a thresholding process on an image, using the dynamic threshold value. CASIA MS Palmprint v1 Database dataset was used to present the examples of algorithm implementation. Morphological dilation and Zhang-Suen algorithms were used to evaluate and compare the effectiveness of the proposed palm vein image thinning algorithm. The results showed that the proposed method makes it possible to compute an image that preserves the vein thickness and closely matches the real vein pattern and is effective when using a template matching algorithm based on perceptual hash functions. © 2023 IEEE.

关键词： Biometrics

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Assessing Machine Learning algorithms for Real-Time Fake Currency Detection 5

Assessing Machine Learning Algorithms for Real-Time Fake Cur...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Lakkshmanan, Ajanthaa Grandhi, Revanth Sai Girish, v. School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798331530013

Counterfeiting poses a significant threat as the circulation of fake currency diminishes the value of genuine notes, thereby disrupting the country's economic stability. Addressing this issue is critical before the proliferation of counterfeit notes becomes unmanageable. Manual detection methods are often unreliable since counterfeit notes are produced with materials and inks that closely resemble the original. Across the country, counterfeit detection is typically carried out using hardware-based systems. However, these methods are time-consuming and struggle to process large volumes efficiently. To address these challenges and streamline the process of counterfeit currency detection, this study proposes an image processing-based computational technique. The objective is to accurately determine whether a given note is genuine or counterfeit, with a high prediction rate. This detection is enhanced using deep learning algorithms, which analyze key attributes such as color, form, paper thickness, serial numbers, and image filters on the currency. The proposed model is trained on a real-time dataset consisting of both genuine and counterfeit notes. Experimental results demonstrate that this method achieves an overall accuracy of 96.41%. © 2024 IEEE.

关键词： Deep learning

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RvCar: An FPGA-Based Simple and Open-Source Mini Motor Car System with a RISC-v Soft Processor

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IEICE TRANSACTIONS ON INFORMATION AND systems 2022年第12期E105D卷 1999-2007页

作者： Kanamori, Takuto Odan, Takashi Hirohata, Kazuki Kise, Kenji Tokyo Inst Technol Sch Comp Tokyo 1528552 Japan

Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmenta-tion. DNN accelerator on FPGA and especially Convolutional Neural Net-work (CNN) is a hot topic. More research and education should be con-ducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RvCar with a RISC-v soft processor and a CNN accelerator. RvCar is suitable for the new entrants who want to learn the implementation of a CNN acceler-ator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in verilog HDL and Systemverilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and ex-ecuted on the RISC-v soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without im-age recognition. We confirm that RvCar can recognize objects and make correct decisions based on the results.

关键词： FPGA motor car Nexys A7 CNN accelerator RISC-v soft processor real time operating system freeRTOS

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Multiple image Augmentations for Enhanced YOLO-based Traffic Sign Detection 2

Multiple Image Augmentations for Enhanced YOLO-based Traffic...

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2nd International Conference on image processing, Computer vision and Machine Learning, ICICML 2023

作者： Long, Pinzhuang Hu, Yichen University of Nottingham Ningbo China Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Mechanical Manufacturing and Material Engineering NottinghamNG7 2RD United Kingdom

ISBN: (纸本)9798350331417

The rapid growth of the automotive industry necessitates the implementation of robust passenger safety measures, especially in the domain of traffic sign recognition for autonomous driving. This study introduces an effective approach to enhance traffic sign detection, with a specific emphasis on the You Only Look Once (YOLO) architecture. The paper addresses the challenges associated with accurately localizing traffic signs by employing diverse image augmentation techniques, including flipping, color inversion, Gaussian blur, affine transformation, and brightness adjustment. Despite computational challenges, particularly in light of YOLOv5's superior accuracy and efficiency, there is still room for further refinement to meet the stringent requirements of autonomous driving research. This research underscores the potential of image augmentation in advancing traffic sign recognition, demonstrating its pivotal role in the development of intelligent transportation systems. In comparison to existing algorithms as YOLO-v3 and YOLO-v5, the proposed technique might successfully reach higher accuracy and less computational power and processing time. © 2023 IEEE.

关键词： Autonomous driving Data augment Traffic signs detection YOLO architecture Introduction

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Enhanced Text-to-image Synthesis Using Generative Adversarial Networks and Skip-Thought vectors for Improved visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

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2024 International Conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.v. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

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