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Cancer Diagnosis from Histopathology Images Using Deep Learning: A Review
Published in Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal, Artificial Intelligence Technologies for Computational Biology, 2023
Vijaya Gajanan Buddhavarapu, J. Angel Arul Jothi
Generally, real-world problems are non-linear. Therefore, in order to remove linearity from the feature maps calculated during the convolution step, non-linear activation functions are used on the output of each neuron. This step ensures that the output of a neuron is not a linear combination of the input(s). The most commonly used activation functions in neural networks are Rectified Linear unit (ReLu) 9.1, Sigmoid (σ(x)), Tanh, and Softmax (fi(x)). These activation functions are given by Eqs. 9.1 - 9.4 where x represents the input.
Role of Data Science in Revolutionizing Healthcare
Published in Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, Data Science, 2022
Yashsingh Manral, Siddhesh Unhavane, Jyoti Kundale
The overall neural network can be made more complex by adding more hidden layers and various filters and complex intermediate layers to make the neural network more efficient and accurate. There is a term called an activation function that is used quite frequently when talking about neural networks. An activation function is simply a mathematical function that defines how the input can be converted into output over the layers that are present in that particular neural network. Many libraries and tools are available which provide easy creation of complex neural networks with few lines of code like Keras and TensorFlow. Once a basic neural network is designed, it is up to the user to make it as easy or as complicated as he or she wishes as per the accuracy. However, because the previous layer receives no feedback, the signals flowing through the neurons and layers are given a weighting, and these signals are then put into a training phase, resulting in a network that can handle any situation [15,19].
Quantum Preprocessing for Deep Convolutional Neural Networks in Atherosclerosis Detection
Published in Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves, Hybrid Quantum Metaheuristics, 2022
Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves
Activation functions are mathematical gates that determine the neuron's output transmitted to the next layer. Some activation functions normalize each neuron output into a range of [1,0] or [−1,1]. Modern neural networks use non-linear activation functions to learn a complex representation between the network's inputs and outputs, as shown in Figure 7.7. The suitable choice of the activation function depends on the nature of the problem to solve. The most widely used activation functions are the Sigmoid, Hyperbolic Tangent, Rectified Linear Unit (ReLU), Leaky ReLU, and SoftMax. The Sigmoid function returns a value close to zero for small values in the argument and close to 1 for large argument values, Sigmoid(x)=11+e−x.
A Corneal Surface Reflections-Based Intelligent System for Lifelogging Applications
Published in International Journal of Human–Computer Interaction, 2023
Tharindu Kaluarachchi, Shamane Siriwardhana, Elliott Wen, Suranga Nanayakkara
The third stage is a CNN of ResNet50 Version 2 (ResNet50V2) (He et al., 2016) architecture that classifies the objects using the cropped and re-scaled images. ResNet50V2 is a very popular and more complex CNN compared to RLN. ResNet50V2 is larger in size and has more features in the architecture. It consists of 5 convolutional blocks and a fully-connected block. Residual connections are added between convolutional blocks and a dropout layer is added in the fully connected block for regularization. The entire network has over 25 million parameters in total. Number of parameters in the output layer is changed according to the number of classes being classified. A softmax activation function is used in the output layer for classification. Figure 4 illustrates the entire data capturing and processing pipeline of our system.
An automated liver tumour segmentation and classification model by deep learning based approaches
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Sayan Saha Roy, Shraban Roy, Prithwijit Mukherjee, Anisha Halder Roy
In a neural network, the activation function is responsible for transforming the summed weighted input from the node into its output. The rectified linear activation function, or ReLU, is a piecewise linear function that directly outputs the input if it is positive, otherwise, it outputs zero. It has become the default activation function for many types of neural networks since it is easier to train and typically produces superior results. Due to the vanishing gradient problem, the sigmoid and hyperbolic tangent activation functions cannot be employed in networks with numerous layers. The rectified linear activation function solves the vanishing gradient problem, allowing models to train quicker and perform better. So ReLU is the default activation function for multilayer perceptron and convolutional neural networks
A Homogeneous Ensemble Classifier for Breast Cancer Detection Using Parameters Tuning of MLP Neural Network
Published in Applied Artificial Intelligence, 2022
Zhiqiang Guo, Lina Xu, Nona Ali Asgharzadeholiaee
In a neural network, the activation function is responsible for transforming the summed weighted input from the node into the activation of the node or output for that input. In this paper, we use the rectified linear activation function. The rectified linear activation function is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero. In addition, to perform the simulation, the parameters of the proposed algorithm are set as follows:, , , , , , , , , . Here, some initial parameter values are obtained from similar studies (Rezaeipanah and Ahmadi 2020; Talatian Azad, Ahmadi, and Rezaeipanah 2021). Other parameters of the proposed algorithm are optimized using Taguchi method (Azadeh et al. 2017).