Leveraging CNN and Transfer Learning for Classification of Histopathology Images

Dubey, Achyut; Singh, Satish Kumar; Jiang, Xiaoyi

doi:10.1007/978-3-031-24367-7_1

Cited by 2 publications

(1 citation statement)

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“…An ensemble subspace discriminant classifier obtains an accuracy of 88.1% for multiclass classification. Dubey et al [ 19 ] proposed a hybrid convolutional neural network architecture for classifying benign and malignant breast lesions in histopathological micrographs. The architecture incorporates a ResNet50 model that has been pre-trained with additional layers that include global average pooling, dropout, and batch normalization.…”

Section: Related Workmentioning

confidence: 99%

Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism

Ashurov,

Chelloug,

Tselykh

et al. 2023

Life

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Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and attention mechanisms to enhance model interpretability and robustness, emphasizing localized features and enabling accurate discrimination of complex cases. Our method involves transfer learning with deep CNN models—Xception, VGG16, ResNet50, MobileNet, and DenseNet121—augmented with the convolutional block attention module (CBAM). The pre-trained models are finetuned, and the two CBAM models are incorporated at the end of the pre-trained models. The models are compared to state-of-the-art breast cancer diagnosis approaches and tested for accuracy, precision, recall, and F1 score. The confusion matrices are used to evaluate and visualize the results of the compared models. They help in assessing the models’ performance. The test accuracy rates for the attention mechanism (AM) using the Xception model on the “BreakHis” breast cancer dataset are encouraging at 99.2% and 99.5%. The test accuracy for DenseNet121 with AMs is 99.6%. The proposed approaches also performed better than previous approaches examined in the related studies.

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Section: Related Workmentioning

confidence: 99%