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. 2024 Jul 26;14(8):792.
doi: 10.3390/jpm14080792.

Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine

Vidhushavarshini Sureshkumar  1 , Rubesh Sharma Navani Prasad  2 , Sathiyabhama Balasubramaniam  3 , Dhayanithi Jagannathan  3 , Jayanthi Daniel  4 , Seshathiri Dhanasekaran  5
Affiliations

Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine

Vidhushavarshini Sureshkumar et al. J Pers Med. .

Abstract

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis.

Keywords: breast cancer; computer-aided diagnosis (CAD); convoluted neural networks; extreme learning machine; mammogram; pectoral muscle removal.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Raw mammogram image and pectoral muscle removed image.
Figure 2
Figure 2
Before and after removal of pectoral muscles.
Figure 3
Figure 3
Pectoral muscle removal.
Figure 4
Figure 4
Results of pectoral muscle removal.
Figure 5
Figure 5
Accuracy of individual versus ensemble ELM.
Figure 6
Figure 6
Training versus validation accuracy of the proposed HCPELM model.
Figure 7
Figure 7
Confusion matrix for HCPELM model.
Figure 8
Figure 8
ROC for HCPELM model.
Figure 9
Figure 9
Comparison of proposed HCPELM and other models.
Figure 10
Figure 10
McNemar test results.
See this image and copyright information in PMC

References

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