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Deep Learning to Diagnose Diseases and Security in 5G Healthcare Informatics
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
CNN models are designed for data from multiple tables or multidimensional arrays to get related properties. This means that the convolutional layer is used to process data of different sizes, including simple and sequential signals, 2D images or 3D spectra for audio and video, or 3D images.
A Review of Automatic Cardiac Segmentation using Deep Learning and Deformable Models
Published in Kayvan Najarian, Delaram Kahrobaei, Enrique Domínguez, Reza Soroushmehr, Artificial Intelligence in Healthcare and Medicine, 2022
Behnam Rahmatikaregar, Shahram Shirani, Zahra Keshavarz-Motamed
Pooling layers usually follow a convolutional layer. They are supposed to reduce the spatial size of the representations to reduce the number of parameters and computations in the network. The most common pooling layer is Max-pooling. As shown in Figure 2.10, in Max-pooling layer with a stride of s and a filter size of f, in each f × f region the maximum pixel is chosen, and the filter will then move s pixels. Another common pooling layer is referred to as average pooling. In average pooling, instead of choosing the maximum, the average of each window is calculated.
Deep Learning Architecture Design for Multi-Organ Segmentation
Published in Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding, Auto-Segmentation for Radiation Oncology, 2021
Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
As discussed previously, the CNN methods input the downsized input image or patch into the convolutional layers and fully connected layers, and subsequent output-predicted label. Shelhamer et al. first proposed a CNN whose last fully connected layer is replaced by a convolutional layer. Since all layers in this CNN are convolutional layers, the new network is named as a fully convolutional network (FCN). Due to the major improvement of deconvolution kernels used to up-sample the feature map, an FCN allows the model to have a dense voxel-wise prediction from the full-size whole volume instead of a patch-wise classification as in a traditional CNN [70]. This segmentation is also called “end-to-end segmentation”. By using an FCN, the segmentation of the whole image can be achieved in just one forward pass. To achieve better localization performance, high-resolution activation maps are combined with up-sampled outputs and then passed to the convolution layers to assemble more accurate output.
Deep learning for assessing liver fibrosis based on acoustic nonlinearity maps: an in vivo study of rabbits
Published in Computer Assisted Surgery, 2022
Jinzhen Song, Hao Yin, Jianbo Huang, Zhenru Wu, Chenchen Wei, Tingting Qiu, Yan Luo
CNN is a deep feed-forward neural network model for processing data with mesh-like features. The sparse connection with the parameter weight sharing methods is integrated by CNN, which results in a significant reduction in the number of training parameters and effective avoidance of algorithm over-fitting. Meanwhile, the back-propagation algorithm is used to update the model parameters. A typical CNN structure consists of feature extraction network and classification network. Convolutional layers and pooling layers are the main components in feature extraction network. Convolutional layer contains a bunch of learnable filters (kernels) to convolve input data to generate feature maps as the input to the next layer. Once the linear transformations are completed, non-linear activation functions are applied to the output of filters. Behind the convolutional layer is the pooling layer. In pooling layer, the dimension and quantity of trainable parameters are diminished by pooling kernels. The process of pooling is aimed to control over-fitting and increase robustness to minor variations in the input data. In classification network, fully connected layer applies a linear transformation to extract and connect features. The softmax classifier is located behind fully connected layers to ensure that the output vector is normalized to sum to 1, reflecting an estimate of the posterior class distribution [25].
Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Sergey Filist, Riad Taha Al-kasasbeh, Olga Shatalova, Altyn Aikeyeva, Nikolay Korenevskiy, Ashraf Shaqadan, Andrey Trifonov, Maksim Ilyash
CNN eliminates the main disadvantage of FNN—the formation of a space of informative features. In CNN, it is generated automatically, but here, too, a number of problems arise related to the empirical selection of CNN meta-parameters. Compared to traditional machine learning methods that separate feature extraction and classification, CNN can automatically extract powerful high-level features and implement classification based on them, which means that CNN combines these two stages of image classification. CNN usually consists of a convolutional layer, an unification layer and a fully connected layer (FC) (Al-Kasasbeh et al., 2019a). Among them, the convolutional layer automatically extracts task-related functions, and the generalizing layer performs the task of descriptor reduction (downsampling). The fully connected layer calculates the confidences for each class. In Wang et al. (2020), a ten-layer CNN is presented, designed to diagnose patients with alcoholism from images of the brain obtained with an MRI scanner. CNN uses a new model of the activation function and additionally introduces a batch normalization block and a dropout block. The results show that the method achieved a sensitivity of 97.73 ± 1.04%, a specificity of 97.69 ± 0.87% and an accuracy of 97.71 ± 0.68%.
Applications of artificial intelligence in clinical management, research, and health administration: imaging perspectives with a focus on hemophilia
Published in Expert Review of Hematology, 2023
Whereas in supervised ML the algorithm is learned from labeled data (e.g. annotated images), in unsupervised ML modeling models automatically extract features and find patterns in the data. In the latter case the model handles datasets without explicit instructions on what to do with it simulating biological neural networks through regular neural networks (RNN). Convolutional neural networks (CNNs) are a type of deep learning neural network algorithm that has at least one convolutional layer allowing obtaining local information with natural spatial invariance (e.g. images, whose meanings do not change under translation) ([2,11,24]. CNNs are often trained with labeled data for supervised learning [2].