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

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Article Type:
Research/Original Article (دارای رتبه معتبر)
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.
Language:
English
Published:
Middle East Journal of Cancer, Volume:15 Issue: 1, Jan 2024
Pages:
40 to 51
magiran.com/p2671549  
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