Breast cancer detection based on simplified deep learning technique with histopathological image using BreaKHis database
Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley, American Geophysical Union (AGU)
2023
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| Subjects: | |
| Online Access: | https://repository.londonmet.ac.uk/8912/1/Radio%20Science%20-%202023%20-%20Toma%20-%20Breast%20Cancer%20Detection%20Based%20on%20Simplified%20Deep%20Learning%20Technique%20With%20Histopathological.pdf https://doi.org/10.1029/2023RS007761 |
| Summary: | Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext-50, ResNext-101, DPN131, DenseNet-169 and NASNet-A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies. |
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