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Titlebook: Computational Intelligence in Oncology; Applications in Diag Khalid Raza Book 2022 The Editor(s) (if applicable) and The Author(s), under e

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Computational Intelligent Systems in Oncology: A Way Toward Translational Healthcare analyses is better when compared to empirical approaches. Moreover, the rise of . (CIS) that encapsulates the power of both—machine learning and deep learning—has allowed some successful applications in clinical cancer research with a quick and robust cancer prediction performance in patients at a
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Application of Convolutional Neural Networks in Cancer Diagnosis), Deep Fully Convolutional Network (DFCNet), Recurrent neural networks, etc. The CNN focus solely on the image-specific features and hence require a lesser number of input parameters. A CNN model reduces the size of the input image vector without losing the features critical for making an accurate
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Predicting Biomarkers and Therapeutic Targets in Cancern of predictive biomarkers incorporating with some drugs derived from natural sources such as trabectedin, cabazitaxel, and alvocidib is a bit slower than usual. Thus, this review paper covers the recent advances of cancer biomarkers which are used to forecast the effectiveness of selected natural c
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