Classification of Breast Cancer from Digital Mammography Using Deep Learning
Open Access
- 15 May 2020
- journal article
- research article
- Published by IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial in INTELIGENCIA ARTIFICIAL
- Vol. 23 (65), 56-66
- https://doi.org/10.4114/intartif.vol23iss65pp56-66
Abstract
Breast cancer is the most frequent in females. Mammography has proven to be the most effective method for the early detection of this type of cancer. Mammographic images are sometimes difficult to understand, due to the nature of the anomalies, the low contrast image and the composition of the mammary tissues, as well as various technological factors such as spatial resolution of the image or noise. Computer-aided diagnostic systems have been developed to increase the accuracy of mammographic examinations and be used by physicians as a second opinion in obtaining the final diagnosis, and thus reduce human errors. Convolutional neural networks are a current trend in computer vision tasks, due to the great performance they have achieved. The present investigation was based on this type of networks to classify into three classes, normal, benign and malignant tumour. Due to the fact that the miniMIAS database used has a low number of images, the transfer learning technique was applied to the Inception v3 pre-trained network. Two convolutional neural network architectures were implemented, obtaining in the architecture with three classes, 86.05% accuracy. On the other hand, in the architecture with two neural networks in series, an accuracy of 88.2% was reached.Keywords
This publication has 20 references indexed in Scilit:
- An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief NetworkJournal of Medical and Biological Engineering, 2017
- A Multi-scale CNN and Curriculum Learning Strategy for Mammogram ClassificationLecture Notes in Computer Science, 2017
- PIndroid: A novel Android malware detection system using ensemble learning methodsComputers & Security, 2017
- National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance ConsortiumRadiology, 2017
- Breast tumor classification in ultrasound images using texture analysis and super-resolution methodsEngineering Applications of Artificial Intelligence, 2017
- Rethinking the Inception Architecture for Computer VisionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- ImageNet Large Scale Visual Recognition ChallengeInternational Journal of Computer Vision, 2015
- Learning and Transferring Mid-level Image Representations Using Convolutional Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Techniques to evaluate the quality of medical imagesAIP Conference Proceedings, 2014
- Design, analysis and classifier evaluation for a CAD tool for breast cancer detection from digital mammogramsInternational Journal of Biomedical Engineering and Technology, 2013