A Comparative Study Of Machine Learning and Deep Learning Models For Breast Cancer Detection

Antony Achandy, Anjaly (2024) A Comparative Study Of Machine Learning and Deep Learning Models For Breast Cancer Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

Breast cancer affects women worldwide and is still a leading killer disease. This is why early diagnosis is extremely important to try and raise the percentage of people surviving this disease. Mammography is the main imaging method used to detect breast cancer, although its interpretation is difficult because breast tissue density varies, which results in missed detection or callback. Recently, the methods of deep learning, especially convolutional neural networks (CNNs), present in most imaging tasks, offer the potential to improve the accuracy rate and decrease the workload for radiologists. The particular dataset for the current work is the MIAS (Mammographic Image Analysis Society) dataset accessible on Kaggle including 322 mammograms labeled with classes (normal, benign, malignant), types of tissue density, and the lesion’s coordinates.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jilani, Musfira UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Life sciences > Medical sciences > Pathology > Tumors > Cancer Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	01 Sep 2025 14:08
Last Modified:	01 Sep 2025 14:08
URI:	https://norma.ncirl.ie/id/eprint/8673

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