Breast cancer detection using deep convolutional neural networks and support vector machines

• Published in: PeerJ (San Francisco, CA) Vol. 7; p. e6201
• Main Authors: Ragab, Dina A., Sharkas, Maha, Marshall, Stephen, Ren, Jinchang
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 28.01.2019; PeerJ, Inc; PeerJ Inc
• Subjects: Breast cancer; Classification; Computer engineering; Computer science; Data Mining and Machine Learning; Datasets; Earth science; Image processing; Mammography; Medical imaging; Medical research; Neural networks; NMR; Nuclear magnetic resonance; Pattern recognition; Principal components analysis; Remote sensing; Segmentation; The computer aided detection; The deep convolutional neural network; The support vector machine; Tumors; Wavelet transforms; The deep convolutional neural network; The support vector machine; The computer aided detection

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tum...