Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach

Mohammed Alshehri

doi:10.3390/math11234725

journal article Open Access Nov 22, 2023

Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach

Mohammed Alshehri

Mathematics Vol. 11 No. 23 pp. 4725 · MDPI AG

View at Publisher Save 10.3390/math11234725

Abstract

Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morphological features were extracted, along with features from deep learning architecture VGG16. Then, we employed a hybrid optimization model, combining the Sparrow Search Algorithm (SSA) and Red Deer Algorithm (RDA), called Hybrid Red Deer with Sparrow optimization (HRDSO), to select the most informative subset of features. For detecting BC, we proposed a new DenseXtNet architecture by combining DenseNet and optimized ResNeXt, which is optimized using the hybrid optimization model HRDSO. The proposed model was evaluated using various performance metrics and compared with existing methods, demonstrating that its accuracy is 97.58% in BC detection. MATLAB was utilized for implementation and evaluation purposes.

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Metrics

11

Citations

36

References

Details

Published: Nov 22, 2023
Vol/Issue: 11(23)
Pages: 4725
License: View

Authors

M

Mohammed Alshehri

Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia

Funding

Deanship of Scientific Research at Majmaah University Award: R-2023-834

Cite This Article

Mohammed Alshehri (2023). Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach. Mathematics, 11(23), 4725. https://doi.org/10.3390/math11234725

Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach

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