Open Access

ARTICLE

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Jiaji Wang¹, Muhammad Attique Khan², Shuihua Wang^1,3, Yudong Zhang^1,3,*

1 School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
3 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Yudong Zhang. Email:

(This article belongs to the Special Issue: Telehealth Monitoring with Man-Computer Interface for Medical Processing)

Computers, Materials & Continua 2023, 76(2), 2201-2216. https://doi.org/10.32604/cmc.2023.041191

Received 14 April 2023; Accepted 09 June 2023; Issue published 30 August 2023

Abstract

Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM) for mammography image classification. The SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, with an accuracy of 94.10% and a sensitivity of 94.30%. A 10-fold cross-validation was performed to ensure the robustness of the results, and the mean and standard deviation of various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all performance indicators, indicating its superior performance. This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images. The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis. This may have significant implications for reducing breast cancer mortality rates.

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