Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation.

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorithms are required to be reliable, robust, and accurate for clinical applications which can be difficult to achieve for some single deep learning methods. In this study, we introduce an ensemble of classifiers for semantic segmentation of medical images. The ensemble of classifiers here is a set of various deep learning-based classifiers, aiming to achieve better performance than using a single classifier. We propose a weighted ensemble method in which the weighted sum of segmentation outputs by classifiers is used to choose the final segmentation decision. We use a swarm intelligence algorithm namely Comprehensive Learning Particle Swarm Optimization to optimize the combining weights. Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. Experiments conducted on some medical datasets of the CAMUS competition on cardiographic image segmentation show that our method achieves better results than both the constituent segmentation models and the reported model of the CAMUS competition

Intelligent human action recognition using an ensemble model of evolving deep networks with swarm-based optimization.

Automatic interpretation of human actions from realistic videos attracts increasing research attention owing to its growing demand in real-world deployments such as biometrics, intelligent robotics, and surveillance. In this research, we propose an ensemble model of evolving deep networks comprising Convolutional Neural Networks (CNNs) and bidirectional Long Short-Term Memory (BLSTM) networks for human action recognition. A swarm intelligence (SI)-based algorithm is also proposed for identifying the optimal hyper-parameters of the deep networks. The SI algorithm plays a crucial role for determining the BLSTM network and learning configurations such as the learning and dropout rates and the number of hidden neurons, in order to establish effective deep features that accurately represent the temporal dynamics of human actions. The proposed SI algorithm incorporates hybrid crossover operators implemented by sine, cosine, and tanh functions for multiple elite offspring signal generation, as well as geometric search coefficients extracted from a three-dimensional super-ellipse surface. Moreover, it employs a versatile search process led by the yielded promising offspring solutions to overcome stagnation. Diverse CNN–BLSTM networks with distinctive hyper-parameter settings are devised. An ensemble model is subsequently constructed by aggregating a set of three optimized CNN–BLSTM​ networks based on the average prediction probabilities. Evaluated using several publicly available human action data sets, our evolving ensemble deep networks illustrate statistically significant superiority over those with default and optimal settings identified by other search methods. The proposed SI algorithm also shows great superiority over several other methods for solving diverse high-dimensional unimodal and multimodal optimization functions with artificial landscapes