2024-12-22 01:51 |
20 p, 726.6 KB |
A study on CNN-based and handcrafted extraction methods with machine learning for automated classification of breast tumors from ultrasound images
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Benaouali, Mohamed (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ;
Bentoumi, Mohamed (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ;
Abed, Mansour (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ;
Mimi, Malika (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ;
Taleb-Ahmed , Abdelmalik (IEMN DOAE UMR CNRS 8520, Universite Polytechnique des Hauts de France, Valenciennes, France)
In this paper, we present an efficient procedure for automatically classifying ultrasound images of benign and malignant breast tumors. We evaluated our approach using four openly available datasets and investigated two categories of feature extraction methods: handcrafted methods (Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG)) and methods based on convolutional neural network (CNN) models. [...]
2024 - 10.5565/rev/elcvia.1887
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 85-104 (Regular Issue)
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2024-11-16 10:01 |
15 p, 19.0 MB |
An Efficient Deep Learning based License Plate Recognition for Smart Cities
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Swati (Patel National Institute of Technology (India)) ;
Dinesh Kawa, Shubh (Patel National Institute of Technology (India)) ;
Kamble, Shubham (Patel National Institute of Technology (India)) ;
Desai, Darshit (Patel National Institute of Technology (India)) ;
Himanshu Karelia, Pratik (Patel National Institute of Technology (India)) ;
Engineer, Pinalkumar (Patel National Institute of Technology (India))
Computer vision algorithm with the amalgamation of deep learning technologies has provided endless possible applications. Currently, with the high load of vehicle traffic it is very difficult to trace and capture vehicular information over traffic surveillance on roads, parking or for safety concerns. [...]
2024 - 10.5565/rev/elcvia.1917
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 50-64 (Regular Issue)
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2024-11-16 10:01 |
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2024-11-16 10:01 |
20 p, 5.4 MB |
A Labeled Array Distance Metric for Measuring Image Segmentation Quality
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Berijanian, Maryam (Michigan State University (USA)) ;
Gensterblum, Katrina (Michigan State University (USA)) ;
Mutlu, Doruk Alp (Michigan State University (USA)) ;
Reagan, Katelyn (University of Wisconsin-Madison (USA)) ;
Hart, Andrew (Michigan State University (USA)) ;
Colbry, Dirk (Michigan State University (USA))
This work introduces two new distance metrics for comparing labeled arrays, which are common outputs of image segmentation algorithms. Each pixel in an image is assigned a label, with binary segmentation providing only two labels ('foreground' and 'background'). [...]
2024 - 10.5565/rev/elcvia.1941
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 65-84 (Regular Issue)
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2024-09-07 08:55 |
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2024-07-13 10:39 |
13 p, 1.1 MB |
Classification of radiological patterns of tuberculosis with a Convolutional neural network in x-ray images
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Trueba Espinosa, Adrian (Universidad Autónoma del Estado de México (México)) ;
Sanchez -Arrazola, Jessica (Universidad Autónoma del Estado de México (México)) ;
Cervantes, Jair (Universidad Autónoma del Estado de México (México)) ;
Garcia-Lamont, Farid (Universidad Autónoma del Estado de México (México)) ;
Ruiz Castilla, José Sergio (Universidad Autónoma del Estado de México (México)) ;
Kantipudi, Karthik (National Institutes of Health (US))
In this paper we propose the classification of radiological patterns with the presence of tuberculosis in X-ray images, it was observed that two to six patterns (consolidation, fibrosis, opacity, opacity, pleural, nodules and cavitations) are present in the radiographs of the patients. [...]
2024 - 10.5565/rev/elcvia.1561
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 47-59 (Regular Issue)
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2024-07-05 06:45 |
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2024-06-08 10:41 |
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2024-04-24 06:00 |
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2024-03-16 09:25 |
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