Computer Aided Diagnosis in Mammography is now an important topic of discussion due to advancement of technologies in both fields of obtaining the mammograms and analysing the mammograms. Digital mammography allows for more sophisticated analysing technologies such as machine learning to solve problems that arises from the variation of diagnosis of mammograms. This project explores the data processing methodologies used to feed data into different types of convolution neural networks (CNN). The different neural networks are chosen as an experimentation to identify the efficiency between each network at classifying mammograms.
Sources of Mammographic images:
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MAMMOGRAPHIC IMAGE ANALYSIS SOCIETY (MIAS) - Mammographic Database, using Version 1.21, 25 August 2015
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J Suckling et al (1994) The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp. 375-378.
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The Digital Database for Screening Mammography (DDSM) from a collaborative effort between Sandia National Laboratories, University of South Florida and Massachusetts General Hospital.
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Eng.usf.edu. (2019). USF Digital Mammography Home Page. [online] Available at: http://www.eng.usf.edu/cvprg/Mammography/Database.html [Accessed 2 Jan. 2019].
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CBIS-DDSM which is a repository based of Digital Database for Screening Mammography.
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Lee, R., Gimenez, F., Hoogi, A., Miyake, K., Gorovoy, M. and Rubin, D. (2017). A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data, 4, p.170177.
Do drop me a mail for the full thesis related to this git repository if anyone is interested!
Computer Aided Detection in Mammography, Predictive classification for Breast Cancer Copyright (C) 2021 jeremyl
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Arleo, E., Hendrick, R., Helvie, M. and Sickles, E. (2017). Comparison of recommendations for screening mammography using CISNET models. Cancer, 123(19), pp.3673-3680.
Balaraman, G. (2019). Numpy Vs Pandas Performance Comparison. [online] Gouthamanbalaraman.com. Available at: http://gouthamanbalaraman.com/blog/numpy-vs-pandas-comparison.html [Accessed 21 Apr. 2019].
Bianco, S., Cadene, R., Celona, L. and Napoletano, P. (2018). Benchmark Analysis of Representative Deep Neural Network Architectures. IEEE Access, 6, pp.64270-64277.
Cancer.org (2019). Understanding Mammogram Reports | Mammogram Results. [online] Cancer.org. Available at: https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection/mammograms/understanding-your-mammogram-report.html [Accessed 21 Apr. 2019].
Cancer.org. (2018). What Does the Doctor Look for on a Mammogram?. [online] Available at: https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection/mammograms/what-does-the-doctor-look-for-on-a-mammogram.html [Accessed 30 Dec. 2018].
Calas, M., Gutfilen, B. and Pereira, W. (2012). CAD e mamografia: por que usar esta ferramenta?. [online] http://www.scielo.br. Available at: http://dx.doi.org/10.1590/S0100-39842012000100011 [Accessed 30 Dec. 2018].
Choi, J., Kim, S., Kang, B., Choi, B., Song, B. and Jung, H. (2013). Mammographic artefacts on Full-Field Digital Mammography. Journal of Digital Imaging, 27(2), pp.231-236.
Christian W. Dawson, 2009. Project in Computing and Information Systems, A Student’s Guide, 2nd Edition. United Kingdom, Dorchester: Dorset Press, Dorset
Docs.opencv.org (2019). OpenCV: Contours in OpenCV. [online] Docs.opencv.org. Available at: https://docs.opencv.org/3.4.3/d3/d05/tutorial_py_table_of_contents_contours.html [Accessed 21 Apr. 2019].
Docs.opencv.org (2019). Hough Line Transform — OpenCV 3.0.0-dev documentation. [online] Docs.opencv.org. Available at: https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html [Accessed 21 Apr. 2019].
Docs.opencv.org (2019). Laplace Operator — OpenCV 2.4.13.7 documentation. [online] Docs.opencv.org. Available at: https://docs.opencv.org/2.4.13.7/doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.html [Accessed 21 Apr. 2019].
Docs.python.org (2019). The Python Standard Library — Python 3.7.3 documentation. [online] Docs.python.org. Available at: https://docs.python.org/3/library/ [Accessed 21 Apr. 2019].
Elmore, J., Wells, C., Lee, C., Howard, D. and Feinstein, A. (1994). Variability in Radiologists' Interpretations of Mammograms. New England Journal of Medicine, 331(22), pp.1493-1499.
Eng.usf.edu. (2019). USF Digital Mammography Home Page. [online] Available at: http://www.eng.usf.edu/cvprg/Mammography/Database.html [Accessed 2 Jan. 2019].
Git-scm.com (2019). Git - Reference. [online] Git-scm.com. Available at: https://git-scm.com/docs [Accessed 21 Apr. 2019].
Henderson, L., Marsh, M., Benefield, T., Pearsall, E., Durham, D., Schroeder, B., Bowling, J., Viglione, C. and Yankaskas, B. (2018). Journal of the American College of Radiology. [online] Jacr.org. Available at: https://www.jacr.org/article/S1546-1440(15)00446-9/pdf [Accessed 30 Dec. 2018].
Jeremy, L. (2019) Preliminary Project Report: Computer Aided Diagnosis in Mammography.
J Suckling et al (1994) The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp. 375-378.
Jordan, M. and Mitchell, T. (2015). Machine learning: Trends, perspectives, and prospects. [online] Cs.cmu.edu. Available at: http://www.cs.cmu.edu/~tom/pubs/Science-ML-2015.pdf [Accessed 30 Dec. 2018].
Keras.io (2019). Model (functional API) - Keras Documentation. [online] Keras.io. Available at: https://keras.io/models/model/ [Accessed 21 Apr. 2019].
Keras.io (2019). Image Preprocessing - Keras Documentation. [online] Keras.io. Available at: https://keras.io/preprocessing/image/ [Accessed 21 Apr. 2019].
Lee, R., Gimenez, F., Hoogi, A., Miyake, K., Gorovoy, M. and Rubin, D. (2017). A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data, 4, p.170177.
Mathworks.com (2019). Access MATLAB Add-On Toolboxes- MATLAB & Simulink. [online] Mathworks.com. Available at: https://www.mathworks.com/help/thingspeak/matlab-toolbox-access.html [Accessed 21 Apr. 2019].
Mathworks.com and Heath, G. (2019). how to save and reuse a trained neural network - MATLAB Answers - MATLAB Central. [online] Mathworks.com. Available at: https://www.mathworks.com/matlabcentral/answers/264160-how-to-save-and-reuse-a-trained-neural-network [Accessed 21 Apr. 2019].
Mathworks.com (2019). Convolutional Neural Network. [online] Mathworks.com. Available at: https://www.mathworks.com/solutions/deep-learning/convolutional-neural-network.html [Accessed 21 Apr. 2019].
Mathworks.com (2019). Image Processing Toolbox. [online] Mathworks.com. Available at: https://www.mathworks.com/products/image.html [Accessed 21 Apr. 2019].
Mathworks.com (2019). Pretrained AlexNet convolutional neural network - MATLAB AlexNet. [online] Mathworks.com. Available at: https://www.mathworks.com/help/deeplearning/ref/AlexNet.html [Accessed 21 Apr. 2019].
Mohamed, H., Mabrouk, M. and Sharawy, A. (2014). Computer aided detection system for micro calcifications in digital mammograms. Computer Methods and Programs in Biomedicine, 116(3), pp.226-235.
National Cancer Institute. (2018). NCI Dictionary of Cancer Terms. [online] Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/breast-cancer [Accessed 30 Dec. 2018].
Nhcs.com.sg. (2018). Breast Cancer Diagnosis: Imaging and Biopsy | SingHealth Duke-NUS Breast Centre. [online] Available at: https://www.nhcs.com.sg/patient-care/conditions-treatments/breast-cancer-diagnosis-treatment/diagnosis [Accessed 30 Dec. 2018].
National Cancer Institute. (2018). NCI Dictionary of Cancer Terms. [online] Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/breast-cancer [Accessed 30 Dec. 2018].
Nehemiah, A. (2019). Deep Learning: Transfer Learning in 10 lines of MATLAB Code. [online] File Exchange Pick of the Week. Available at: https://blogs.mathworks.com/pick/2017/02/24/deep-learning-transfer-learning-in-10-lines-of-matlab-code/ [Accessed 21 Apr. 2019].
OpenCV (2019). About - OpenCV library. [online] Opencv.org. Available at: https://opencv.org/about.html [Accessed 17 Apr. 2019].
Pressman R.S., 2010. Software Engineering: a Practitioner’s Approach, 7th Edition. United States of America, New York: McGraw Hill.
PyPI (2019). numpy. [online] PyPI. Available at: https://pypi.org/project/numpy/ [Accessed 21 Apr. 2019].
PyPI (2019). pandas. [online] PyPI. Available at: https://pypi.org/project/pandas/ [Accessed 21 Apr. 2019].
PyPI (2019). pydicom. [online] PyPI. Available at: https://pypi.org/project/pydicom/ [Accessed 21 Apr. 2019].
PyPI (2019). pypng. [online] PyPI. Available at: https://pypi.org/project/pypng/ [Accessed 21 Apr. 2019].
PyPI (2019). PyPI – the Python Package Index. [online] PyPI. Available at: https://pypi.org/ [Accessed 21 Apr. 2019].
PyPI (2019). tqdm. [online] PyPI. Available at: https://pypi.org/project/tqdm/ [Accessed 21 Apr. 2019].
PyPI (2019). opencv-python. [online] PyPI. Available at: https://pypi.org/project/opencv-python/ [Accessed 21 Apr. 2019].
Ruizendaal, R. (2019). Deep Learning #3: More on CNNs & Handling Overfitting. [online] Towards Data Science. Available at: https://towardsdatascience.com/deep-learning-3-more-on-cnns-handling-overfitting-2bd5d99abe5d [Accessed 21 Apr. 2019].
Salleh, A. (2018). 1 in 4 breast cancers 'not threatening' › Analysis (ABC Science). [online] Abc.net.au. Available at: http://www.abc.net.au/science/articles/2009/11/11/2739398.htm [Accessed 30 Dec. 2018].
Singaporecancersociety.org.sg. (2018). What is Cancer?. [online] Available at: https://www.singaporecancersociety.org.sg/learn-about-cancer/cancer-basics/what-is-cancer.html [Accessed 30 Dec. 2018].
Sommerville, I, 2010. Software Engineering, 9th Edition. Harlow: Pearson Education
Stack Overflow and edn (2019). Is the class generator (inheriting Sequence) thread safe in Keras/TensorFlow?. [online] Stack Overflow. Available at: https://stackoverflow.com/questions/52932406/is-the-class-generator-inheriting-sequence-thread-safe-in-keras-TensorFlow [Accessed 21 Apr. 2019].
Steger, C., Ulrich, M. and Wiedemann, C. (2017). Machine vision algorithms and applications. 2nd ed.
TensorFlow (2019). TensorFlow. [online] TensorFlow. Available at: https://www.TensorFlow.org/install [Accessed 21 Apr. 2019].
TensorFlow (2019). Keras | TensorFlow Core | TensorFlow. [online] TensorFlow. Available at: https://www.TensorFlow.org/guide/keras [Accessed 21 Apr. 2019].
TensorFlow (2019). Hub with Keras | TensorFlow Core | TensorFlow. [online] TensorFlow. Available at: https://www.TensorFlow.org/tutorials/images/hub_with_keras [Accessed 21 Apr. 2019].
Tfhub.dev (2019). TensorFlow Hub. [online] Tfhub.dev. Available at: https://tfhub.dev/s?module-type=image-feature-vector [Accessed 21 Apr. 2019]. TensorFlow (2019). Save and restore models | TensorFlow Core | TensorFlow. [online] TensorFlow. Available at: https://www.TensorFlow.org/tutorials/keras/save_and_restore_models [Accessed 21 Apr. 2019].
Yassin, N., Omran, S., El Houby, E. and Allam, H. (2018). Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Computer Methods and Programs in Biomedicine, 156, pp.25-45.