outputs/Background and Literature survey of Medical Image Analysis Im going to use CNN and RNN as models/research-6026870446088756682.txt

[{"title": "Convolutional Neural Network in Medical Image Analysis: A Review - Springer", "href": "https://link.springer.com/article/10.1007/s11831-023-09898-w", "body": "Convolutional neural networks (CNNs) have been used for visual applications since the late 1980s. Despite a few scattered applications, their implication was dormant until the mid-2000s."}, {"title": "Convolutional Neural Networks for Medical Image Analysis: Full Training ...", "href": "https://pubmed.ncbi.nlm.nih.gov/26978662/", "body": "Abstract. Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images."}, {"title": "Convolutional Neural Networks for Medical Image Analysis: Full Training ...", "href": "https://arxiv.org/pdf/1706.00712", "body": "natural language processing to hyperspectral image processing and to medical image analysis. The main power of a CNN lies in its deep architecture [5]-[8], which allows for extracting a set of discriminating features at multiple levels of abstraction. However, training a deep CNN from scratch (or full train-ing) is not without complications [9]."}]