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Antony Sami, Joy, Arumugam, Umamakeswari. (1402). An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer. سامانه مدیریت نشریات علمی, 15(1), 40-51. doi: 10.30476/mejc.2023.97317.1857
Joy Christy Antony Sami; Umamakeswari Arumugam. "An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer". سامانه مدیریت نشریات علمی, 15, 1, 1402, 40-51. doi: 10.30476/mejc.2023.97317.1857
Antony Sami, Joy, Arumugam, Umamakeswari. (1402). 'An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer', سامانه مدیریت نشریات علمی, 15(1), pp. 40-51. doi: 10.30476/mejc.2023.97317.1857
Antony Sami, Joy, Arumugam, Umamakeswari. An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer. سامانه مدیریت نشریات علمی, 1402; 15(1): 40-51. doi: 10.30476/mejc.2023.97317.1857

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

Middle East Journal of Cancer
مقاله 5، دوره 15، شماره 1 - شماره پیاپی 57، فروردین 2024، صفحه 40-51    اصل مقاله (4.19 M)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/mejc.2023.97317.1857
نویسندگان
Joy Christy Antony Sami* 1؛ Umamakeswari Arumugam2
1Department of Computer Science, School of Computing, SASTRA Deemed to be University, Thanjavur, India
2School of Computing, SASTRA Deemed to be University, Thanjavur, India
چکیده
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.
کلیدواژه‌ها
Biopsy؛ Mammography؛ Machine learning؛ Cytology؛ Deep learning
مراجع
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