An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer

Antony Sami, Joy Christy; Arumugam, Umamakeswari

doi:10.30476/mejc.2023.97317.1857

An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer

Document Type : Original Article(s)

Authors

Joy Christy Antony Sami ¹

Umamakeswari Arumugam ²

¹ Department of Computer Science, School of Computing, SASTRA Deemed to be University, Thanjavur, India

² School of Computing, SASTRA Deemed to be University, Thanjavur, India

https://doi.org/10.30476/mejc.2023.97317.1857

Abstract

Background: Detecting breast cancer in its early stages remains a significant challenge in the present context and is a leading cause of death among women, primarily due to delayed identification. This paper presents a practical and accurate approach based on deep learning to identify breast cancer in cytology images.
Method: The analytical approach leverages knowledge from a related problem through a technique known as transfer learning. Convolutional neural networks (CNNs) are employed due to their remarkable performance on large datasets. Image classification architectures such as Google network (GoogleNet), Visual geographical group network (VGGNet), residual network (ResNet), and dense convolution network (DenseNet) are utilized in this approach. By applying transfer learning, the images are classified into two categories: those containing cancer cells and those without them. The performance of the proposed ensemble method is evaluated using a breast cytology image dataset.
Results: The results of our proposed ensemble framework outperform conventional CNN models in terms of precision, recall, and F1 measures, achieving an impressive 86% prediction accuracy. Visual representations of validation graphs for each classifier demonstrate that the ensemble framework surpasses the performance of pre-trained CNN architectures.
Conclusion: Combining the outcomes of conventional CNN architectures into an ensemble framework enhances early breast cancer detection, leading to a reduction in mortality through timely medical interventions.

Keywords

Biopsy

Mammography

Machine learning

Cytology

Deep learning

Subjects

Cancer Diagnosis

How to cite this article:

Antony Sami JC, Arumugam U. An ensemble deep learning model for the detection and classification of breast cancer. Middle East J Cancer. 2024;15(1):40-51. doi: 10. 30476/mejc.2023.97317.1857.

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