Deep learning based breast cancer detection using MRI images

Author

Kadzombe, Vaida

Title

Deep learning based breast cancer detection using MRI images

Abstract

Breast cancer represents a critical global health issue, underscoring the importance of early detection to enhance treatment efficacy and increase survival rates. It is characterized by the uncontrolled growth of abnormal cells in the breast, leading to the formation of tumors. If not promptly addressed, these tumors can metastasize, posing a fatal risk. Breast cancer typically originates within the milk ducts or the milk-producing lobules of the breast. Utilizing advancements in deep learning and artificial intelligence presents an opportunity to enhance breast cancer detection methods. This study proposes the utilization of a deep learning system to identify breast cancer through the analysis of MRI images. It describes the goals, methods, and expected results. Breast cancer is a prevalent issue for women worldwide, causing considerable healthcare and financial challenges for Zimbabwe. Despite various detection methods, challenges persist, including delayed healthcare seeking and limited access to early detection facilities. Deep learning techniques offer promise in diagnosing breast cancer earlier and more accurately, particularly when integrated with MRI imaging. Such methods require less human intervention and can detect abnormalities that may be missed by conventional procedures. The proposed system employs advanced neural network architectures to analyze MRI images, automatically extracting features indicative of breast cancer lesions. Preliminary testing indicates promising results, with an accuracy rate of 93.6% surpassing comparable works in the literature. Future enhancements may involve utilizing more powerful computational machines, augmenting training data, and striking a balance between precision and recall to optimize model performance. By integrating these recommendations, this research aims to advance breast cancer detection, ultimately contributing to improved patient outcomes and personalized healthcare.

Date

June 2024

Publisher

BUSE

Keywords

Breast cancer

Convolutional neural network

Tumor

MRI images

Deep learning

Supervisor

Mr Chaka