2022
DOI: 10.1155/2022/8517706
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Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network

Abstract: Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDB… Show more

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Cited by 131 publications
(2 citation statements)
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References 28 publications
(32 reference statements)
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“…To address this problem, a unique framework is developed. Combining CNNs with traditional numerical methods is what this method [22] tries to do to make showing complicated thermal dynamics more accurate, useful, and flexible. Table 3 displays the simulation parameters for the four techniques in this dataset.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…To address this problem, a unique framework is developed. Combining CNNs with traditional numerical methods is what this method [22] tries to do to make showing complicated thermal dynamics more accurate, useful, and flexible. Table 3 displays the simulation parameters for the four techniques in this dataset.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…It also needs topic expertise as well as the support of a radiologist. Deep learning (DL) models, on the other hand, may extract features from an input dataset and generate an adaptive learning process depending on the goal output [14]. The DL techniques significantly accelerate data processing and pattern extraction activities, enabling for the reuse of approaches Various studies have been conducted to look at breast cancer images from various angles [15].…”
Section: Introductionmentioning
confidence: 99%