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

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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Bibliographische Detailangaben
Hauptverfasser: Warid Islam, Meredith Jones, Rowzat Faiz, Negar Sadeghipour, Yuchen Qiu, Bin Zheng
Format: Artikel
Sprache:English
Veröffentlicht: MDPI AG 2022-09-01
Schriftenreihe:Tomography
Schlagworte:
computer-aided diagnosis (CAD) scheme of mammograms
convolutional block attention module (CBAM)
residual network (ResNet)
breast lesion classification
Online Zugang:https://www.mdpi.com/2379-139X/8/5/200

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

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