Book ChapterDOI
Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier
Pinaki Ranjan Sarkar,Deepak Mishra,Gorthi R. K. Sai Subrahmanyam +2 more
- pp 25-36
TLDR
This paper proposes to use a CNN architecture to do both of the feature extraction and classification task, and it is observed that high-level features lead to a better diagnosis and convolutional neural network (CNN) is the best-known model to extract high- level features.Abstract:
Due to the difficulties of radiologists to detect micro-calcification clusters, computer-aided detection (CAD) system is much needed. Many researchers have undertaken the challenge of building an efficient CAD system and several feature extraction methods are being proposed. Most of them extract low- or mid-level features which restrict the accuracy of the overall classification. We observed that high-level features lead to a better diagnosis and convolutional neural network (CNN) is the best-known model to extract high-level features. In this paper, we propose to use a CNN architecture to do both of the feature extraction and classification task. Our proposed network was applied to both MIAS and DDSM databases, and we have achieved accuracy of \(99.074\%\) and \(99.267\%\), respectively, which we believe that is the best reported so far.read more
Citations
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Journal ArticleDOI
Multi-Tasking U-Shaped Network for Benign and Malignant Classification of Breast Masses
TL;DR: Wang et al. as mentioned in this paper proposed a Multi-Tasking U-shaped Network (MT-UNet), which combines truncated normalization method and adaptive histogram equalization method to enhance the contrast of image.
Proceedings ArticleDOI
Towards Automated Breast Mass Classification using Deep Learning Framework
TL;DR: An automated deep CAD system performing both the functions: mass detection and classification of breast mass classification including the extraction of wavelet features is proposed in this work.
Journal ArticleDOI
Hybrid computer aided diagnostic system designs for screen film mammograms using <scp>DL</scp> ‐based feature extraction and <scp>ML</scp> ‐based classifiers
TL;DR: In this article , a hybrid CAD system with VGG19 Network model acting as feature extractor and ANFC-LH classifier can be employed for the differential diagnosis of benign as well as malignant mammographic masses using screen film mammographic (SFM) images.
References
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Proceedings Article
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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TL;DR: The American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival.
Proceedings ArticleDOI
Convolutional networks and applications in vision
TL;DR: New unsupervised learning algorithms, and new non-linear stages that allow ConvNets to be trained with very few labeled samples are described, including one for visual object recognition and vision navigation for off-road mobile robots.