Journal ArticleDOI
Breast tumor classification in ultrasound images using texture analysis and super-resolution methods
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TLDR
It is shown that the super-resolution-based approach improves the performance of the evaluated texture methods and thus outperforms the state of the art in benign/malignant tumor classification.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2017-03-01. It has received 89 citations till now. The article focuses on the topics: Local binary patterns & Phase congruency.read more
Citations
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Journal ArticleDOI
Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework
Vivek Kumar Singh,Mohamed Abdel-Nasser,Farhan Akram,Hatem A. Rashwan,Md. Mostafa Kamal Sarker,Nidhi Pandey,Santiago Romani,Domenec Puig +7 more
TL;DR: An efficient automated method for tumor segmentation in BUS images based on a contextual information-aware conditional generative adversarial learning framework that achieves competitive results compared with state-of-the-art segmentation models in terms of Dice and IoU metrics is proposed.
Journal ArticleDOI
Breast tumors recognition based on edge feature extraction using support vector machine
TL;DR: The results show that edge-based features can well describe breast tumors in ultrasound images, and have the potential to be used in breast ultrasound computer-aided design.
Journal ArticleDOI
Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping Network
TL;DR: A hybrid multi-branch convolutional neural network based on feature cropping method for feature extraction and classification of thyroid nodule ultrasound images and a weighted cross-entropy loss function to train the proposed binary-classification network is proposed.
Journal ArticleDOI
Effect of despeckle filtering on classification of breast tumors using ultrasound images
TL;DR: It can be concluded that the proposed hybrid CAD system design could be used as a second opinion tool in clinical setting with optimal performance for classification of benign and malignant breast tumors with a classification accuracy of 96.0%.
Book ChapterDOI
Hybrid Approach for Classification of Electroencephalographic Signals Using Time–Frequency Images With Wavelets and Texture Features
TL;DR: Experimental evaluation performed on two publicly available EEG datasets suggests that the proposed hybrid approach to analyze the time–frequency image of EEG signals is a proficient and powerful method for more accurate and earlier detection of epilepsy.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI
The Fractal Geometry of Nature
TL;DR: A blend of erudition (fascinating and sometimes obscure historical minutiae abound), popularization (mathematical rigor is relegated to appendices) and exposition (the reader need have little knowledge of the fields involved) is presented in this article.