Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

<b>Background:</b> The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. <b>Objective:</b> This study...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Warid Islam, Meredith Jones, Rowzat Faiz, Negar Sadeghipour, Yuchen Qiu, Bin Zheng
Μορφή:	Άρθρο
Γλώσσα:	English
Έκδοση:	MDPI AG 2022-09-01
Σειρά:	Tomography
Θέματα:	computer-aided diagnosis (CAD) scheme of mammograms convolutional block attention module (CBAM) residual network (ResNet) breast lesion classification
Διαθέσιμο Online:	https://www.mdpi.com/2379-139X/8/5/200

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https://www.mdpi.com/2379-139X/8/5/200

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

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