Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)
Xia Li, Xi Shen, Yongxia Zhou, Xiuhui Wang, Tie‐Qiang Li
Abstract
In this study, we proposed a novel convolutional neural network (CNN) architecture for classification of benign and malignant breast cancer (BC) in histological images. To improve the delivery and use of feature information, we chose the DenseNet as the basic building block and interleaved it with the squeeze-and-excitation (SENet) module. We conducted extensive experiments with the proposed framework by using the public domain BreakHis dataset and demonstrated that the proposed framework can produce significantly improved accuracy in BC classification, compared with the state-of-the-art CNN methods reported in the literature.
Topics & Concepts
Convolutional neural networkPattern recognition (psychology)Breast cancerComputer scienceBlock (permutation group theory)Artificial intelligenceFeature (linguistics)Domain (mathematical analysis)Contextual image classificationCancerImage (mathematics)MedicineMathematicsInternal medicineGeometryMathematical analysisLinguisticsPhilosophyAI in cancer detectionDigital Imaging for Blood DiseasesRadiomics and Machine Learning in Medical Imaging