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Machine Learning
Published in Michael Ljungberg, Handbook of Nuclear Medicine and Molecular Imaging for Physicists, 2022
The Artificial Neural Network can be viewed as a generalization of the linear logistic regression. The main building blocks of an artificial neural network are Affine functions Aw,m,n from ℝm to ℝn. An affine function from ℝm to ℝn is Aw,m,n(x) = Ax + b. The weights w consists of the m × n entries of the matrix A as well as the n entries of the vector b.Activation functions T from ℝn to ℝn.The softmax function S, given by Eq. 11.9.
Machine Learning for Disease Classification: A Perspective
Published in Kayvan Najarian, Delaram Kahrobaei, Enrique Domínguez, Reza Soroushmehr, Artificial Intelligence in Healthcare and Medicine, 2022
Jonathan Parkinson, Jonalyn H. DeCastro, Brett Goldsmith, Kiana Aran
Recent years have witnessed dramatic growth in the diversity of neural network model structures described in the literature. The simplest structure is the multi-layer perceptron (MLP), sometimes also called a fully connected neural network. It consists of a sequential arrangement of layers; the first layer processes the input, while subsequent layers process the output of the previous layer. Each layer follows the general form:Where G is a nonlinear function selected from a variety of “activation functions”, W is a weight matrix, x is an input column vector and b is a vector of bias terms (analogous to a y-intercept). Common activation functions in modern neural networks include the sigmoid function, the rectified linear unit (ReLU) and the softmax function (Goodfellow et al., 2016). SoftMax function. The term deep learning usually refers to neural networks with multiple layers. In many neural network architectures, the output of a layer is normalized before being passed to the next layer, which has been shown to speed up convergence during training and can reduce the risk of overfitting (Ioffe & Szegedy, 2015).
Classification of Breast Thermograms using a Multi-layer Perceptron with Back Propagation Learning
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
The activation function used at the output for the nth data sample is SoftMax transfer function that normalizes its input into 2 probabilities. This ensures that the sum of the probabilities of the output vector S(Z) is 1. The MLP will output the class with highest measure of certainty as the true class (benign or malignant). The SoftMax function and its derivative are expressed as follows,
Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals
Published in International Journal of Neuroscience, 2023
To learn the different levels of data abstraction, the networks in the deep neural networks are decomposed into multiple layers of processing. The Hybrid architecture of the Convolution neural network (CNN) and Long Short Term Memory (LSTM) has been implemented for the classification of emotions (Valence and arousal). The hybrid model is composed of alternating layers of CNN and several recurrent layers of LSTM. The LSTM-RNN has been implemented due to the highly non-linear and temporal nature of EEG signals. To make EEG data suitable for CNN analysis, the highly structured data need to be formulated which consists of 2D data frames (time*channel) associated with a specific trial. The spatial and temporal features of the given input EEG signal has been extracted using CNN and LSTM respectively. The fully connected layer converts the output size of the previous layers into several emotion classes as shown in Figure 3. The softmax layer computes the probability of each emotion over all target classes and finally, the output layer computes the cost function. The softmax function is defined as:
Detection of brain lesion location in MRI images using convolutional neural network and robust PCA
Published in International Journal of Neuroscience, 2023
Mohsen Ahmadi, Abbas Sharifi, Mahta Jafarian Fard, Nastaran Soleimani
One of the most used functions in the classification is the SoftMax function. In some cases, the SVM function is also used. But since the SoftMax function gives us a more accurate result of the probability of a class, it is, therefore, more suitable for classification. The probability of each class is calculated from the following formula.
A step towards the application of an artificial intelligence model in the prediction of intradialytic complications
Published in Alexandria Journal of Medicine, 2022
Ahmed Mustafa Elbasha, Yasmine Salah Naga, Mai Othman, Nancy Diaa Moussa, Hala Sadik Elwakil
Furthermore, in the third analysis, eight neurons (7 events and no event) in the output layer were applied for the categorical classification as shown in Figure 2. In this case, the SoftMax function was applied as the output layer activation function, as it is a multi-classification problem. Categorical cross-entropy was used as a loss function.