Informatics, Volume 9, Issue 4
2022 December - 29 articles
Cover Story: Accurately distinguishing malignant tumors from benign ones enables patients to receive lifesaving treatments on time. However, doctors currently do not identify 10% to 30% of breast cancers during regular assessment. We propose an automated method for binary classification of breast cancer tumors as either malignant or benign that utilizes a Bag of Deep Multi-Resolution Convolutional Features (BoDMCF) extracted from histopathological images at four resolutions (40X, 100X, 200X and 400X) by three pre-trained state-of-the-art deep CNN models: ResNet-50, EfficientNetb0, and Inception-v3. The BoDMCF were pooled using global average pooling and classified using the Support Vector Machine (SVM) classifier. The proposed approach outperforms the prior state of the art. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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