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• W4285503611 abstract "Deep learning algorithms help achieve promising results in diagnosis of cancer patient. However, obtaining patient confidential data and medical images of cancer patient from medical hospitals and diagnosis centre is challenging thus preventing the deep learning usage in analysis of medical data of patient. Among woman, Breast cancer is prevailing cancer and it has become common health problem. The classification of Breast cancer focuses on identifying breast tumours sub-classes. Deep learning is among the current trends in computer science research and it enhances neural networks. The layers in neural networks are more, deep which allows higher abstraction levels and help to get accurate analysis of data. Google’s recently proposed architecture-EfficientNet, has less parameters which results in faster convergence for classification using ImageNet which showed that EfficientNet outperformed prevailing architectures such as DenseNet and ResNet. This research work include comparing the performance of EfficientNetV2-B0 architecture with DenseNet and ResNet architectures and few others for multiple class tumour classification on BreakHis dataset, which contains around 8000 histopathology images of breast that vary in magnification. This research work offers a methods that is based on transfer learning that improves existing architecture in multi-class classification by relying on pretrained DCNN trained on ImageNet dataset. Pretrained weights of EfficientNetV2-B0 are transferred as initial weights and the model is fine tuned in order to identify different benign and malignant tissue samples in tumor cells and classify them into multiclass. The model proposed has been tested with optimised hyperparameters irrespective of magnification of the images and was able to achieve accuracy upto 98%. The results surpass earlier studies in terms of accuracy across the performance metrics defined for CAD systems of breast cancer based on histological images." @default.
• W4285503611 created "2022-07-15" @default.
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• W4285503611 date "2022-05-27" @default.
• W4285503611 modified "2023-09-27" @default.
• W4285503611 title "Histopathological Image Classification of Breast Cancer using EfficientNet" @default.
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• W4285503611 doi "https://doi.org/10.1109/incet54531.2022.9824351" @default.
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