Categorization of Carcinogenic Abnormalities in Digital Mastography Using Deep Learning Algorithms

Wafaa Razzaq

Categorization of Carcinogenic Abnormalities in Digital Mastography Using Deep Learning Algorithms

Wafaa Razzaq
International Journal of Computational and Electronic Aspects in Engineering
Volume 4: Issue 4, October 2023, pp 119-126

Author's Information

Wafaa Razzaq ¹

Corresponding Author

¹College of Nursing, University of Thi-Qar, Nasiriyah, Iraq

wafaa_razzaq@utq.edu.iq

Article -- Peer Reviewed

First online on – 12 October 2023

Open Access article under Creative Commons License

Cite this article –Wafaa Razzaq “Categorization of Carcinogenic Abnormalities in Digital Mastography Using Deep Learning Algorithms”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 4, Issue 4, pp. 119-126, 2023.

https://doi.org/10.26706/ijceae.4.4.20231004

Abstract:-

Cancer has been considered a serious disease for centuries and globally is one of the most prevalent conditions, which has been reinforced in recent years; breast cancer is the most common type of cancer in women and the second leading cause of cancer death worldwide. This mortality rate has been reduced thanks to various early detection techniques, mainly mastography and correct analysis. Currently, digital mastography can be computer assisted and this research takes as a reference the application of image preprocessing and various assembled algorithms in conjunction with Deep learning to improve the efficiency of detection. Through datasets generated and applying LogitBoost and AtributeSelectedClassifier algorithms in conjunction with Deep Learning, it analyzes the histogram of the images belonging to MIAS Dataset, obtaining competitive results of 88.37%.

Index Terms:-

Breast cancer, micro-calcification, classification, deep learning

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