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• W3096194915 abstract "Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics." @default.
• W3096194915 created "2020-11-09" @default.
• W3096194915 creator A5012808984 @default.
• W3096194915 creator A5040845826 @default.
• W3096194915 date "2020-10-30" @default.
• W3096194915 modified "2023-09-27" @default.
• W3096194915 title "C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification" @default.
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• W3096194915 doi "https://doi.org/10.48550/arxiv.2011.00081" @default.
• W3096194915 hasPublicationYear "2020" @default.
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