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
Virtual Interpolation Images of Tumor Development and Growth on Breast Ultrasound Image Synthesis With Deep Convolutional Generative Adversarial Networks.
Tomoyuki Fujioka,Kazunori Kubota,Kazunori Kubota,Mio Mori,Leona Katsuta,Yuka Kikuchi,Koichiro Kimura,Mizuki Kimura,Mio Adachi,Goshi Oda,Tsuyoshi Nakagawa,Yoshio Kitazume,Ukihide Tateishi +12 more
TL;DR: This work seeks to generate realistic synthetic breast ultrasound images and express virtual interpolation images of tumors using a deep convolutional generative adversarial network (DCGAN).
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Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning
TL;DR: Convolutional neural networks (CNNs) are employed to present the traditional convolutionAL neural network (TCNN) and supported convolutional Neural Network (SCNN) approaches and the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass).
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Classification of Breast Cancer from Digital Mammography Using Deep Learning
TL;DR: This investigation used convolutional neural networks to classify into three classes, normal, benign and malignant tumour, based on the miniMIAS database used, and the transfer learning technique was applied to the Inception v3 pre-trained network.
Journal ArticleDOI
Amniotic fluid segmentation based on pixel classification using local window information and distance angle pixel
TL;DR: This research proposes a novel pixel classification model to separate amniotic fluid from other objects with a limit on the specified window size to solve the issue of selection of the most profound areas of improper and withdrawal points.
Journal ArticleDOI
Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms
Jesus A. Basurto-Hurtado,Irving A. Cruz-Albarran,Manuel Toledano-Ayala,Mario Alberto Ibarra-Manzano,Luis A. Morales-Hernandez,Carlos A. Perez-Ramirez +5 more
TL;DR: This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the imagegeneration and processing are presented and discussed.
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.
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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.