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Journal articles on the topic 'Region based convolutional neural networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

M., Sushma Sri, Rajendra Naik B., and Jaya Sankar K. "Object Detection Based on Faster R-Cnn." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 3 (2021): 72–76. https://doi.org/10.35940/ijeat.C2186.0210321.

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In recent years there is rapid improvement in Object detection in areas of video analysis and image processing applications. Determing a desired object became an important aspect, so that there are many numerous of methods are evolved in Object detection. In this regard as there is rapid development in Deep Learning for its high-level processing, extracting deeper features, reliable and flexible compared to conventional techniques. In this article, the author proposes Object detection with deep neural networks and faster region convolutional neural networks methods for providing a simple algor

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Bollimuntha, Mahalakshmi. "Region-Based Convolutional Neural Networks Based Automated Detection and Classification of Diabetic Retinopathy." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45749.

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Abstract - One of the main causes of vision impairment in diabetic patients is diabetic retinopathy (DR), which is brought on by long-term elevated blood sugar levels damaging the retinal blood vessels. Early and accurate detection is essential for effective treatment. Deep learning-based approaches have shown significant promise in automated Diabetic Retinopathy (DR) diagnosis by leveraging advanced feature extraction and classification techniques. This study explores a deep learning framework utilizing a Region-based Convolutional Neural Network (RCNN) for Diabetic Retinopathy (DR) detection

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Ye, Mengqi, and Lijun Zhang. "Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (December 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/8650059.

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There are great differences in financial and economic development in different regions. In different time series and different regions, the effects of financial depth and width on economic development are also different. This paper selects neural network to establish the economic benefit model of financial depth and breadth, which can deeply explore the relationship between financial data and economic data. In order to determine the optimal convolutional neural network parameters, the optimal convolutional neural network parameters are determined through comparative simulation analysis. The co

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WANG Chun-zhe, 王春哲, 安军社 AN Jun-she, 姜秀杰 JIANG Xiu-jie, and 邢笑雪 XING Xiao-xue. "Region proposal optimization algorithm based on convolutional neural networks." Chinese Optics 12, no. 6 (2019): 1348–61. http://dx.doi.org/10.3788/co.20191206.1348.

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Deng, Zihan, and Ang Li. "Object Detection Algorithms Based on Convolutional Neural Networks." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 243–51. http://dx.doi.org/10.54097/vyfg4e34.

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Object detection is a fundamental and challenging task in computer vision, and it has attracted much attention from researchers worldwide. In recent years, deep learning technology has made remarkable progress and enabled new possibilities for object detection. Convolutional neural networks (CNNs), which are powerful tools for feature extraction and representation learning, have become the dominant approach for object detection, surpassing the traditional methods. This article reviews the development history of CNNs and their applications to object detection. It also introduces and compares tw

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Yan, Shiyang, Yizhang Xia, Jeremy S. Smith, Wenjin Lu, and Bailing Zhang. "Multiscale Convolutional Neural Networks for Hand Detection." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9830641.

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Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper,

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Pramila, P. V., and Atani Yamini. "Precision Analysis of Salient Object Detection in Moving Video Using Region Based Convolutional Neural Network Compared Over Optical Character Recognition." ECS Transactions 107, no. 1 (2022): 14001–15. http://dx.doi.org/10.1149/10701.14001ecst.

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To detect the number plate of the vehicles which violates the traffic rules using optical character recognition compared over region based convolutional networks. Materials and methods: In this work, number plates are identified by optical character recognition with the sample size of 22 and region based convolutional neural network of sample size 22. The number plate of the vehicle will be detected and converted into string format. Results: A prediction accuracy of 96.4% using the optical character recognition method was achieved, while the region based convolutional neural network was 94.2.%

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Xu, Yuelei, Mingming Zhu, Shuai Li, Hongxiao Feng, Shiping Ma, and Jun Che. "End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks." Remote Sensing 10, no. 10 (2018): 1516. http://dx.doi.org/10.3390/rs10101516.

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Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learnin

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Hu, Donghui, Qiang Shen, Shengnan Zhou, Xueliang Liu, Yuqi Fan, and Lina Wang. "Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks." Security and Communication Networks 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2314860.

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Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image an

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de Silva, Akila, Issei Mori, Gregory Dusek, James Davis, and Alex Pang. "Automated rip current detection with region based convolutional neural networks." Coastal Engineering 166 (June 2021): 103859. http://dx.doi.org/10.1016/j.coastaleng.2021.103859.

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Rafique, Muhammad Aasim, Witold Pedrycz, and Moongu Jeon. "Vehicle license plate detection using region-based convolutional neural networks." Soft Computing 22, no. 19 (2017): 6429–40. http://dx.doi.org/10.1007/s00500-017-2696-2.

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Islam, Atiqul, Mark Tee Kit Tsun, Lau Bee Theng, and Caslon Chua. "Region-based convolutional neural networks for occluded person re-identification." International Journal of Advances in Intelligent Informatics 10, no. 1 (2024): 49. http://dx.doi.org/10.26555/ijain.v10i1.1125.

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In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to det

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C, Rupesh, and Madhuri T. "SPINAL CORD DEFORMITY DETECTION USING REGION-BASED CONVOLUTIONAL NEURAL NETWORKS." ICTACT Journal on Data Science and Machine Learning 6, no. 1 (2024): 739–42. https://doi.org/10.21917/ijdsml.2024.0151.

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Spinal cord deformities, including scoliosis, kyphosis, and lordosis, significantly impact the quality of life and often require early and precise diagnosis to prevent further complications. Traditional diagnostic methods such as X-ray interpretation and manual measurements are time-consuming and prone to subjective errors. To address these challenges, this work proposes a deep learning-based approach leveraging Region-Based Convolutional Neural Networks (RCNN) for automatic spinal cord deformity detection and classification. The method processes medical imaging data, extracts critical spinal

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Asensio Ramos, A., and C. J. Díaz Baso. "Stokes inversion based on convolutional neural networks." Astronomy & Astrophysics 626 (June 2019): A102. http://dx.doi.org/10.1051/0004-6361/201935628.

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Context. Spectropolarimetric inversions are routinely used in the field of solar physics for the extraction of physical information from observations. The application to two-dimensional fields of view often requires the use of supercomputers with parallelized inversion codes. Even in this case, the computing time spent on the process is still very large. Aims. Our aim is to develop a new inversion code based on the application of convolutional neural networks that can quickly provide a three-dimensional cube of thermodynamical and magnetic properties from the interpreation of two-dimensional m

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Kim, Yong-Ho, and Jung-Ryul Lee. "Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing." Structural Health Monitoring 18, no. 5-6 (2019): 2020–39. http://dx.doi.org/10.1177/1475921719830328.

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A typical aircraft engine consists of fans, compressors, turbines, and so on, and each is made of multiple layers of blades. Discovering the site of damages among the large number of blades during aircraft engine maintenance is quite important. However, it is impossible to look directly into the engine unless it is disassembled. For this reason, optical equipment such as a videoscope is used to visually inspect the blades of an engine through inspection holes. The videoscope inspection method has some obvious drawbacks such as the long-time attention on microscopic video feed and high labor in

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Li, Aoyong, and Kay W. Axhausen. "Short-term Traffic Demand Prediction using Graph Convolutional Neural Networks." AGILE: GIScience Series 1 (July 15, 2020): 1–14. http://dx.doi.org/10.5194/agile-giss-1-12-2020.

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Abstract. Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, which has attracted attention from the taxi industry and Mobility-on-Demand systems. Accurate predictions enable operators to dispatch their vehicles in advance, satisfying both drivers and passengers. This study aims to predict traffic demand over the entire city based on the Graph convolutional network (GCNN). Specially, we divide the study area into several non-overlap sub-regions. Each sub-region is treated as a node, and a traffic demand graph is constructed. Then, we build three

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Yin, Shoulin, Ye Zhang, and Shahid Karim. "Region search based on hybrid convolutional neural network in optical remote sensing images." International Journal of Distributed Sensor Networks 15, no. 5 (2019): 155014771985203. http://dx.doi.org/10.1177/1550147719852036.

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Currently, big data is a new and hot issue. Particularly, the rapid growth of the Internet of Things causes a sharp growth of data. Enormous amounts of networking sensors are continuously collecting and transmitting data to be stored and processed in the cloud, including remote sensing data, environmental data, and geographical data. And region is regarded as the very important object in remote sensing data, which is mainly researched in this article. Region search is a crucial task in remote sensing process, especially for military area and civilian fields. It is difficult to fast search regi

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Amirah Hanani Jamil, Fitri Yakub, Azizul Azizan, Shairatul Akma Roslan, Sheikh Ahmad Zaki, and Syafiq Asyraff Ahmad. "A Review on Deep Learning Application for Detection of Archaeological Structures." Journal of Advanced Research in Applied Sciences and Engineering Technology 26, no. 1 (2022): 7–14. http://dx.doi.org/10.37934/araset.26.1.714.

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Over the last few years, archaeologist have started to look at automated object detection for searching of potential historical sites, using object identification methods that includes neural network-based and non-neural network-based approaches. However, there is a scarcity of reviews on Convolutional Neural Networks (CNN) based Deep Learning (DL) models for object detection in the archaeological field. The purpose of this review is to examine existing research that has been implemented in the area of ancient structures object detection using Convolutional Neural Networks. Notably, CNN based

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Pinzon Arenas, Javier O., Robinson Jimenez Moreno, and Paula C. Useche Murillo. "Hand gesture recognition by means of region-based convolutional neural networks." Contemporary Engineering Sciences 10, no. 27 (2017): 1329–42. http://dx.doi.org/10.12988/ces.2017.710154.

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This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, an

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Zhou, Yuxiang, Xiang Cai, Qingfeng Zhao, Zhoufang Xiao, and Gang Xu. "Quadrilateral Mesh Generation Method Based on Convolutional Neural Network." Information 14, no. 5 (2023): 273. http://dx.doi.org/10.3390/info14050273.

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The frame field distributed inside the model region characterizes the singular structure features inside the model. These singular structures can be used to decompose the model region into multiple quadrilateral structures, thereby generating a block-structured quadrilateral mesh. For the generation of block-structured quadrilateral mesh for two-dimensional geometric models, a convolutional neural network model is proposed to identify the singular structure inside the model contained in the frame field. By training the network model with a large number of model region decomposition data obtain

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Douass, S., and M. Ait Kbir. "Deep learning approach for land use images classification." E3S Web of Conferences 351 (2022): 01043. http://dx.doi.org/10.1051/e3sconf/202235101043.

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CNN (convolutional neural networks) are a category of neural networks that are majorly used for image classification and recognition. This Deep Learning (DL) technique is used to solve complex problems, particularly for environmental protection, its approaches have affected several domains without exception, geospatial world is one vised domain. In this paper we aim to classify aerial images of Tangier region, city located in north of Morocco, by using pixel based image classification with convolutional Neural Networks. Flickr API is used to get our test images dataset. These images are used a

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Zhao, Hanqing, Matthijs Souilljee, Pavlos Pavlidis, and Nikolaos Alachiotis. "Genome-wide scans for selective sweeps using convolutional neural networks." Bioinformatics 39, Supplement_1 (2023): i194—i203. http://dx.doi.org/10.1093/bioinformatics/btad265.

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Abstract Motivation Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection. Results We present ASDEC (https://github.com/pephco/ASDEC), a neural-network-based fra

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Akila, V., Kasi Glory, Pinnamaraju Sanjana Varma, Puchakayala Lakshmi Hemanjili, Tutari Vijaya Lohitha, and T. Sheela. "City Cleanliness Drive Web Portal Using Region Based Convolutional Neural Networks." E3S Web of Conferences 309 (2021): 01127. http://dx.doi.org/10.1051/e3sconf/202130901127.

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Identification or detection of object played an important role in Computer Vision, in implementations like city construction process Managers had often wasted lot of their energy, time and resources in cleaning up the garbage, which was unexpectedly showed up. When deep network systems increased its complexity, the systems are constrained by the training data availability. Due to this, Open CV, Google AI released the Open images dataset publicly, so that the research and development would happen in study and analysis of images. As a result, virtual street cleanliness been at most important in

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Zalluhoglu, Cemil, and Nazli Ikizler-Cinbis. "Region based multi-stream convolutional neural networks for collective activity recognition." Journal of Visual Communication and Image Representation 60 (April 2019): 170–79. http://dx.doi.org/10.1016/j.jvcir.2019.02.016.

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Nakazawa, Atsushi, Kanako Harada, Mamoru Mitsuishi, and Pierre Jannin. "Real-time surgical needle detection using region-based convolutional neural networks." International Journal of Computer Assisted Radiology and Surgery 15, no. 1 (2019): 41–47. http://dx.doi.org/10.1007/s11548-019-02050-9.

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Adu-Gyamfi, Yaw Okyere, Sampson Kwasi Asare, Anuj Sharma, and Tienaah Titus. "Automated Vehicle Recognition with Deep Convolutional Neural Networks." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (2017): 113–22. http://dx.doi.org/10.3141/2645-13.

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In recent years there has been growing interest in the use of nonintrusive systems such as radar and infrared systems for vehicle recognition. State-of-the-art nonintrusive systems can report up to eight classes of vehicle types. Video-based systems, which arguably are the most popular nonintrusive detection systems, can report only very coarse classification levels (up to four classes), even with the best-performing vision systems. The present study developed a vision system that can report finer vehicle classifications according to FHWA’s scheme and is also comparable to other nonintrusive r

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Raihanah Abdani, Siti, Syed Mohd Zahid Syed Zainal Ariffin, Nursuriati Jamil, and Shafaf Ibrahim. "3D-based Convolutional Neural Networks for Medical Image Segmentation." International journal of electrical and computer engineering systems 16, no. 5 (2025): 347–63. https://doi.org/10.32985/ijeces.16.5.1.

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Medical image segmentation is essential for disease screening and diagnosis, particularly through techniques like anatomical and lesion segmentation that can be used to isolate critical regions of interest. However, manual segmentation is labor-intensive, costly, and susceptible to subjective bias, underscoring the need for automation. Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced segmentation accuracy and efficiency. With the introduction of 3D imaging, research has evolved from 2D CNNs to 3D CNNs, which leverage inter-slice information to improv

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Rajesh, Ravula, Singadi Akhil Reddy, Gandikota Varma Devraj, Raghuram Bhukya, Harika Dasari, and Naaram Srichandana. "Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 465–70. http://dx.doi.org/10.17762/ijritcc.v11i6.7784.

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It's important to note that Alzheimer's disease can also affect individuals over the age of 60, and in fact, the risk of developing Alzheimer's increases with age. Additionally, while deep learning approaches have shown promising results in detecting Alzheimer's disease, they are not the only techniques available for diagnosis and treatment. That being said, using Region-based Convolutional Neural Network (RCNN) for efficient feature extraction and classification can be a valuable tool in detecting Alzheimer's disease. This new approach to identifying Alzheimer's disease could lead to a more a

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Jiang, Quanchun, Olamide Timothy Tawose, Songwen Pei, et al. "Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging." Big Data and Cognitive Computing 3, no. 2 (2019): 31. http://dx.doi.org/10.3390/bdcc3020031.

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In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-supervised semantic segmentation. Most methods use manual labeling. In this paper, super-pixels with similar features are combined using the relationship between each pixel after super-pixel segmentation to form a plurality of super-pixel blocks. Rough predictions are generated by the fully convolutional networks (FCN) so that certain super-pixel blo

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ABAS, SHAKIR M. "Diagnosing the Leukemia using Faster Region based Convolutional Neural Network." Journal of Applied Science and Technology Trends 3, no. 02 (2022): 35–38. http://dx.doi.org/10.38094/jastt302134.

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It is difficult to building deep learning algorithms for identifying chronic diseases. One of the must difficulties facing the system of diagnosing leukemia is the irregular shape and twisted nucleus in white blood cells (WBCs) without cleaning and segmentation of cells by Appling filters. Moreover, it is challenge to identify and classify the WBC at once time which is considered the essential step of leukemia diagnosing. This paper proposed system only based on deep learning algorithms. The modified Faster R-CNN (Faster Region based-Convolutional Neural Networks) algorithm is used to detect a

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Sushma Sri, V., V. Hima Sailu, U. Pradeepthi, P. Manogyna Sai, and Dr M. Kavitha. "Disease Detection using Region-Based Convolutional Neural Network and ResNet." Data and Metadata 2 (December 4, 2023): 135. http://dx.doi.org/10.56294/dm2023135.

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In recent times, various techniques have been employed in agriculture to address different aspects. These techniques encompass strategies to enhance crop yield, identify hidden pests, and implement effective pest reduction methods, among others. Presented in this study a novel strategy which focuses on identification of plant leaf infections in agricultural fields using drones. By employing cameras on drones with high resolution, we take precise pictures of plant leaves, ensuring comprehensive coverage of the entire area. These images serve as datasets for Deep Learning algorithms, including C

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Adetunji, Olusogo Julius, Ibrahim Adepoju X. Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, and Adedayo Aladejobi Sobowale. "Flood Image Classification using Convolutional Neural Networks." ABUAD Journal of Engineering Research and Development (AJERD) 6, no. 2 (2023): 113–21. http://dx.doi.org/10.53982/ajerd.2023.0602.11-j.

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Flood disaster is a natural disaster that leads to loss of lives, properties damage, devastating effects on the economy and environment; therefore, there should be effective predictive measures to curb this problem. Between the years 2002- 2023, flood has caused death of over 200,000 people globally and occurred majorly in resource poor countries and communities. Different machine learning approaches have been developed for the prediction of floods. This study develops a novel model using convolutional neural networks (CNN) for the prediction of floods. Important parameters such as standard de

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Mai Magdy, Fahima A. Maghraby, and Mohamed Waleed Fakhr. "A 4D Convolutional Neural Networks for Video Violence Detection." Journal of Advanced Research in Applied Sciences and Engineering Technology 36, no. 1 (2023): 16–25. http://dx.doi.org/10.37934/araset.36.1.1625.

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As global crime has escalated, surveillance cameras have become widespread and will continue to proliferate. Due to the large amount of video, there must be systems that automatically look for suspicious activity and send out an online alert if they find it. This paper presents a deep learning architecture based on video-level four-dimensional convolution neural networks. The suggested architecture consists of residual blocks, which are combined with three-dimensional Convolutional Neural Networks (3D CNNs). The architecture aims to learn short-term and long-term representations of spatiotempo

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Fan, Weiwei, Feng Zhou, Xueru Bai, Mingliang Tao, and Tian Tian. "Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images." Remote Sensing 11, no. 23 (2019): 2862. http://dx.doi.org/10.3390/rs11232862.

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Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neu

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Li, Hu, Yabo Wang, and Yao Nan. "Motion Fatigue State Detection Based on Neural Networks." Computational Intelligence and Neuroscience 2022 (March 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/9602631.

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Aiming at the problem of fatigue state detection at the back of sports, a cascade deep learning detection system structure is designed, and a convolutional neural network fatigue state detection model based on multiscale pooling is proposed. Firstly, face detection is carried out by a deep learning model MTCNN to extract eye and mouth regions. Aiming at the problem of eye and mouth state representation and recognition, a multiscale pooling model (MSP) based on RESNET is proposed to train the eye and mouth state. In real-time detection, the state of the eye and mouth region is recognized throug

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Lei, Cheng-Wei, Li Zhang, Tsung-Ming Tai, Chen-Chieh Tsai, Wen-Jyi Hwang, and Yun-Jie Jhang. "Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks." Applied Sciences 11, no. 17 (2021): 7838. http://dx.doi.org/10.3390/app11177838.

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This study aims to develop a novel automated computer vision algorithm for quality inspection of surfaces with complex patterns. The proposed algorithm is based on both an autoencoder (AE) and a fully convolutional neural network (FCN). The AE is adopted for the self-generation of templates from test targets for defect detection. Because the templates are produced from the test targets, the position alignment issues for the matching operations between templates and test targets can be alleviated. The FCN is employed for the segmentation of a template into a number of coherent regions. Because

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Diab, Amal G., Nehal Fayez, and Mervat Mohamed El-Seddek. "Accurate skin cancer diagnosis based on convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1429. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1429-1441.

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<span>Although melanoma is not the most common type of skin cancer, it is supposed to extend to other areas of the body if not early diagnosed. Melanoma is the deadliest form of skin cancer and accounts for about 75% of deaths associated with skin cancer. The present study introduces an automated technique for skin cancer prediction, detection, and diagnosis including trending noninvasive and nonionizing techniques that combines deep learning methods to diagnose melanoma with high accuracy. Computer-aided diagnosis (CAD) using medical images is utilized to distinguish benign and malignan

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Diab, Amal G., Nehal Fayez, and Mervat Mohamed El-Seddek1. "Accurate skin cancer diagnosis based on convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1429–41. https://doi.org/10.11591/ijeecs.v25.i3.pp1429-1441.

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Although melanoma is not the most common type of skin cancer, it is supposed to extend to other areas of the body if not early diagnosed. Melanoma is the deadliest form of skin cancer and accounts for about 75% of deaths associated with skin cancer. The present study introduces an automated technique for skin cancer prediction, detection, and diagnosis including trending noninvasive and nonionizing techniques that combines deep learning methods to diagnose melanoma with high accuracy. Computer-aided diagnosis (CAD) using medical images is utilized to distinguish benign and malignant tumors, wh

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Devahema, D., S. M. K. Shyaam, M. Karthikeyan, V. S. Vishal, and G. Pushpak. "Object Detection for Blind People Using Faster Region-Based Convolutional Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 11 (2020): 4915–19. http://dx.doi.org/10.1166/jctn.2020.9206.

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Age is not just a number as human body also ages as time pass by. The time passes our vision can also begin to deteriorate as a study suggests 82% of blind people in 39 million blind population are about 50 years and older. So the device suggested can help people to walk without support of others as it uses image recognition by machine learning and informs the user about the obstacle ahead. Such a way of using machine learning has already been applied in self-driving cars and it is quite effective. And also the device can help disable people who were born blind. The camera will be mounted on t

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Cao, Shaomeng, Zhiye Chen, Congsheng Li, Chuanfeng Lv, Tongning Wu, and Bin Lv. "Landmark‐based multi‐region ensemble convolutional neural networks for bone age assessment." International Journal of Imaging Systems and Technology 29, no. 4 (2019): 457–64. http://dx.doi.org/10.1002/ima.22323.

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Poornima, M. "Convolutional Neural Networks Based Paddy Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2991–3000. http://dx.doi.org/10.22214/ijraset.2024.60546.

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Abstract: This study proposes a novel approach for detecting paddy leaf diseases through the integration of Convolutional Neural Networks (CNNs). The process begins by converting input RGB images into HSV color space, where the saturation component is extracted. Subsequently, a binary image is generated, followed by background removal to enhance the accuracy of segmentation. The background-removed image is then converted back to the HSV color space for further processing. Kmeans segmentation is applied to segment the leaf regions effectively. The CNN architecture is employed for classification

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Kim, JongBae. "Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations." Symmetry 11, no. 7 (2019): 882. http://dx.doi.org/10.3390/sym11070882.

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The number and range of the candidate vehicle license plate (VLP) region affects the result of the VLP extraction symmetrically. Therefore, in order to improve the VLP extraction rate, many candidate VLP regions are selected. However, there is a problem that the processing time increases symmetrically. In this paper, we propose a method that allows detecting a vehicle license plate in the real-time mode. To do this, the proposed method makes use of the region-based convolutional neural network (R-CNN) method and morphological operations. The R-CNN method is a deep learning method that selects

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Deng, Lu, Hong-Hu Chu, Peng Shi, Wei Wang, and Xuan Kong. "Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks." Applied Sciences 10, no. 7 (2020): 2528. http://dx.doi.org/10.3390/app10072528.

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Cracks are often the most intuitive indicators for assessing the condition of in-service structures. Intelligent detection methods based on regular convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years; however, these methods exhibit unsatisfying performance on the detection of out-of-plane cracks. To overcome this drawback, a new type of region-based CNN (R-CNN) crack detector with deformable modules is proposed in the present study. The core idea of the method is to replace the traditional regular convolution and pooling operation wit

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Liu, Hanqing, Xiaojun Li, Jin Wei, and Xiaodong Kang. "Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm." Discrete Dynamics in Nature and Society 2022 (April 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/4494411.

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Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is prop

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Liu, Hanqing, Xiaojun Li, Jin Wei, and Xiaodong Kang. "Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm." Discrete Dynamics in Nature and Society 2022 (April 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/4494411.

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Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is prop

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Kim, Hyeri, and Boonleng Cheong. "Robust Velocity Dealiasing for Weather Radar Based on Convolutional Neural Networks." Remote Sensing 15, no. 3 (2023): 802. http://dx.doi.org/10.3390/rs15030802.

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Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range (ra) is important for surveillance but a high aliasing velocity (va) is also important to obtain storm dynamics unambiguously. However, the ra and va are inversely related to pulse repetition time. This “Doppler dilemma” is more challenging at shorter wavelengths. The proposed algorithm employs a CNN (convolutional neural network), which is widely used in image classification, to tackle the velocity dealiasing issue. Velocity aliasing can be converted to a classification

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Nguyen, Hoanh. "An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks." Advances in Multimedia 2022 (August 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/8852142.

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Benefited from deep convolutional neural networks, various license plate detection methods based on deep networks have been proposed and achieved significant improvements compared with traditional methods. However, the high computational cost due to complex structures prevents these methods from being deployed in real-world applications. This paper proposes an efficient license plate detection method based on lightweight deep convolutional neural networks for improving the detection speed. To extract high-level features from input images, this paper designs a lightweight feature pyramid genera

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Singh, Anurag, Naresh Kumar, and Seifedine Kadry. "ThreatNet: advanced threat detection, region-based convolutional neural network framework." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1007. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1007-1015.

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It is critical for many countries to ensure public safety in detecting and identifying threats in a night, <span lang="EN-US">commercial places, border areas and public places. Majority of past research in this area has focused on the use of image-level categorization and object-level detection techniques. As an X-ray and thermal security image analysis strategy, object separation can considerably improve automatic threat detection when used in conjunction with other techniques. In order to detect possible threats, the effects of introducing segmentation deep learning models into the thr

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Rachmatullah, Muhammad Naufal, and Akhtiar W. Arum. "Video Based Fish Species Detection Using Faster Region Convolution Neural Network." Computer Engineering and Applications Journal 12, no. 2 (2023): 114–22. http://dx.doi.org/10.18495/comengapp.v12i2.467.

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Fish recognition and classification represent significant challenges in marine biology and agriculture, promising fields for advancing research. Despite advancements in real-time data collection, underwater fish recognition and classification still require improvement due to challenges such as variations in fish size and shape, image quality issues, and environmental changes. Feature learning approaches, particularly utilizing convolutional neural networks (CNNs), have shown promise in addressing these challenges. This study focuses on video-based fish species classification, employing a featu

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Wang, Hui. "Neural Network-Oriented Big Data Model for Yoga Movement Recognition." Computational Intelligence and Neuroscience 2021 (October 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/4334024.

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The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved

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