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- W4361799072 abstract "Deep learning (DL) is the most rapidly expanding in the current scenario. For image analysis and categorization, deep neural networks (DNNs) are presently the most extensively utilized technology. DNN designs include GoogleNet, residual networks, and AlexNet, among others. Breast cancer is seen as a major problem that endangers the lives and health of women. Ultrasonography or MRI scanning methods are used to diagnose breast cancer disease. Imaging methods used for diagnosis include digital mammography, ultrasonography, magnetic resonance imaging, and infrared thermography. The primary objective is to investigate different deep learning algorithms for recognizing breast cancer-affected imageries. The best models provide accuracy for the 2, 4, and classifications on cancer datasets. No previous research is carried out for the current model investigation. Early detection and screening are critical for effective therapy. The following is a synopsis of recent progress in mammograms and identification, as well as a discussion of technological advancements. An effective test result should meet the following requirements: performance, sensitivity, specificity, precision, recall, and low cost. The experimental settings for every study on breast cancer histopathology images are thoroughly reviewed and deliberated in this article." @default.
- W4361799072 created "2023-04-05" @default.
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- W4361799072 date "2023-01-01" @default.
- W4361799072 modified "2023-09-27" @default.
- W4361799072 title "A Review on Deep Learning Approaches for Histopathology Breast Cancer Classification" @default.
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- W4361799072 doi "https://doi.org/10.1007/978-981-19-8563-8_35" @default.
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