Background:Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca).Methods:Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance.Results:An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques.Conclusions:Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

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This article aims to present a novel direction of arrival (DOA) estimation strategy for smart antenna in multipath environment. The smart antenna is composed of 2 main parts: the DOA estimator and the switched‐beam system. In the first part, a DOA estimation method based on convolutional neural network (CNN) has been implemented. The CNN is capable to select the desired radiation beams of the switched‐beam antenna without knowing the number of source signals coming from different directions, and in the case of noncoherent and coherent signals. Simulation results have been presented to show the effectiveness of the proposed intelligent approach.

In this paper, a relevant automated electromagnetic (EM) optimization method and a novel, fast, and accurate artificial neural network are proposed for the efficient CAD modeling of microwave circuits. We lay the groundwork for our investigation of radial wavelet neural networks WNNs trained by BFGS (Broyden‐Fletcher‐Goldfarb‐Shanno) and LBFGS (limited memory BFGS) algorithms and their application to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialization for the neural network, and thus improved convergence of the learning algorithm. The optimization method combines a rigorous and accurate global EM analysis of device performed with a finite‐element method (FEM) and a fast neural model deduced from its segmented EM analysis. Finally, experimental results, which confirm the validity of the WNN model, and good agreement between theoretical optimization results and experimental ones are reported. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 297–306, 1999.

In this paper, a relevant automated electromagnetic (EM) optimization method and a novel, fast, and accurate artificial neural network are proposed for the efficient CAD modeling of microwave circuits. We lay the groundwork for our investigation of radial wavelet neural networks WNNs trained by BFGS (Broyden-Fletcher-Goldfarb-Shanno) and LBFGS (limited memory BFGS) algorithms and their application to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialization for the neural network, and thus improved convergence of the learning algorithm. The optimization method combines a rigorous and accurate global EM analysis of the device performed with a finite-element method (FEM) and a fast neural model deduced from its segmented EM analysis. Finally, experimental results, which confirm the validity of the WNN model, and good agreement between theoretical optimization results and experimental ones are reported.