Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis...

ABSTRACT: The paper presents a new optoelectronic CNN system which consists of two integrated circuits connected through the flip-chip bonding: a) the photonic GaAs matrix of receivers and emitter; b) the CMOS chip of digital CNN cells with programmable 5-bit weights and threshold currents. This solution is easier to design and is more reliable than analogue circuits. The main advantages of our...

A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware processors and all the pixels of the i...

Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indis...

Fisher Vector (FV) and deep Convolutional Neural Network (CNN) are two popular approaches for extracting effective image representations. FV aggregates local information (e.g., SIFT) and have been state-of-the-art before the recent success of deep learning approaches. Recently, combination of FV and CNN has been investigated. However, only the aggregation of SIFT has been tested. In this work, ...

In this paper, we propose a multi-channel convolutional neural network-long shortterm memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter. Unlike a conventional CNN, the proposed model applies a multi-channel strategy that uses several filters of different length to extract active local n-gram features i...

There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis. In this paper, rather than preprocessing vibration signals to denoise or extract features, we investigate the usage of CNNs on raw signals; in particular, we test the accuracy of CNNs as classifiers on bearing fault data, by varying the configurations of the CNN from one-lay...

Based on a simple circuit model of a tunneling phase logic (TPL) element that is driven by a sinusoidal voltage source and biased by a DC voltage source, we present simulations of operations in cellular nonlinear networks (CNN) that could potentially be used to perform general computations in 2D arrays of simple, locally connected nanoscale devices. Some examples are presented to demonstrate th...

In this paper, we propose an ensemble classification approach to the Pedestrian Detection (PD) problem, resorting to distinct input channels and Convolutional Neural Networks (CNN). This methodology comprises two stages: piq the proposals extraction, and piiq the ensemble classification. In order to obtain the proposals, we apply several detectors specifically developed for the PD task. Afterwa...

In this paper we present a new approach for the analysis of the spatio-temporal dynamics of brain electrical activity in epilepsy with Cellular Neural Networks (CNN). We have shown in recent investigations [1] that the dimension D 2(k;m) of brain electrical activity can be approximated by a function of CNN cell outputs. These results obtained with CNN, having non steady-states, were sensitive t...