A Detail Process for CAD Based Breast Cancer Detection
- Authors
-
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Priyanka Banerjee
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India -
Samir Kumar Bandyopadhyay
Academic Advisor, The Bhawanipur Education Society College, Kolkata, India
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- Keywords:
- Breast cancer, mammography, risk factors, Pectoral Muscle.
- Abstract
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Breast cancer is known to cause high mortality unless detected in time. Early detection during the onset of the disease can prevent mortality. Early detection can prevent the spreading of the disease thus providing a healthy life to senior citizens. Mammographic screening and surgical biopsy will yield huge number of images to be deciphered by radiologists and pathologists respectively. MIAS dataset is sufficiently large to conduct experimental analysis. Moreover, the dataset contains 322 mammogram images of different size, shape and morphology. This paper discussed about breast cancer detection and diagnosis process. (Word count -91 words).
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- References
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Albregtsen F, Nielsen B, Danielsen HE. Adaptive gray level run length features from class distance matrices. Proceedings - International Conference on Pattern Recognition 2000; 15(3): 738-741. https://doi.org/10.1109/icpr.2000.903650
American Cancer Society. Breast Cancer Facts & Figures 2019-2020. American Cancer Society 2019; pp. 1-44. https://doi.org/10.1007/978-1-4614-6439-6_151-2
Chandrasekhar R, Attikiouzel Y. Segmentation of the pectoral muscle edge on mammograms by tunable parametric edge detection. Advances in Signal Processing and Computer Technologies 2001; pp. 55-60. Retrieved from https://pdfs.semanticscholar.org/dec0/c39a34f1a4b8879cd9fc97a69cc9f2c4e65f.pdf?_ga=2.263117132.1774612510.1577001857-1752445732.1577001857
Bahlmann C, Patel A, Johnson J, Ni J, Chekkoury A, Khurd P, Weinstein R. Automated detection of diagnostically relevant regions in H&E stained digital pathology slides. Medical Imaging 2012: Computer-Aided Diagnosis 2012; 8315: 831504. https://doi.org/10.1117/12.912484
Ball JE, Bruce LM. Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation. Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale 2007; pp. 4973-4978. https://doi.org/10.1109/IEMBS.2007.4353457
Ali MA, Czene K, Eriksson L, Hall P, Humphreys K. Breast tissue organisation and its association with breast cancer risk. Breast Cancer Research 2017; 19(1): 1-13. https://doi.org/10.1186/s13058-017-0894-6
Oliveira HCR, de Mencattini A, Casti P, Martinelli E, Natale C di Catani JH, Vieira MAC. Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography. SPIE Medical Imaging 2018. Retrieved from https://www.spiedigitallibrary. org/conference-proceedings-of-spie/10575/105752P/ Reduction-of-false-positives-in-a-CAD-scheme-for-automated/10.1117/12.2293388.short?tab=ArticleLink
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- Published
- 31-12-2019
- Issue
- Vol. 8 No. 1 (2019)
- Section
- Articles
How to Cite
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