Human Sleeping Posture Recognition Based on Sleeping Pressure Image

Wan, Qilong; Zhao, Huijie; Li, Jie; Xu, Peng

doi:10.1109/jsen.2022.3225290

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…By reducing the number of parameters, SqueezeNet can improve the computational efficiency of the network and make it easier to deploy on devices with limited resources. Support vector machine (SVM) [16,17], a commonly utilized ML technique for classification tasks, has achieved significant success in various applications [18][19][20]. SVM is useful in situations where the data is non-linearly separable, as it can employ kernel functions to map the input data into a higher-dimensional feature space where linear separation is possible.…”

Section: Introductionmentioning

confidence: 99%

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Wang,

Attique Khan,

Wang

et al. 2023

Computers, Materials &Amp; Continua

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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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