Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.British Heart Foundation Accelerator Award, UKRoyal Society International Exchanges Cost Share Award, UK RP202G0230Hope Foundation for Cancer Research, UK RM60G0680Medical Research Council Confidence in Concept Award, UK MC_PC_17171MINECO/FEDER, Spain/Europe RTI2018-098913-B100 A-TIC-080-UGR1

A non-linear VAD for noisy environments

This paper deals with non-linear transformations for improving the performance of an entropy-based voice activity detector (VAD). The idea to use a non-linear transformation has already been applied in the field of speech linear prediction, or linear predictive coding (LPC), based on source separation techniques, where a score function is added to classical equations in order to take into account the true distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if the signal is clean, the estimated entropy is essentially the same; if the signal is noisy, however, the frames transformed using the score function may give entropy that is different in voiced frames as compared to nonvoiced ones. Experimental evidence is given to show that this fact enables voice activity detection under high noise, where the simple entropy method fails

Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.This work has received research funding from the Spanish government (www.micinn.es) under project TEC2012 34306 (DiagnoSIS, Diagnosis by means of Statistical Intelligent Systems, 70K€) and projects P09-TIC-4530 (300K€) and P11-TIC-7103 (156K€) from the Andalusian government (http://www.juntadeandalucia.es/organismo​s/economiainnovacioncienciayempleo.html)

Noise Cancellation using Selectable Adaptive Algorithm for Speech in Variable Noise Environment

Some of the teething problems associated in the use of two-sensor noise cancellation systems are the nature of the noise signals—a problem that imposes the use of highly complex algorithms in reducing the noise. The usage of such methods can be impractical for many real time applications, where speed of convergence and processing time are critical. At the same time, the existing approaches are based on using a single, often complex adaptive filter to minimize noise, which has been determined to be inadequate and ineffective. In this paper, a new mechanism is proposed to reduce background noise from speech communications. The procedure is based on a two-sensor adaptive noise canceller that is capable of assigning an appropriate filter adapting to properties of the noise. The criterion to achieve this is based on measuring the eigenvalue spread based on the autocorrelation of the input noise. The proposed noise canceller (INC) applies an adaptive algorithm according to the characteristics of the input signal. Various experiments based on this technique using real-world signals are conducted to gauge the effectiveness of the approach. Initial results illustrated the system capabilities in executing noise cancellation under different types of environmental noise. The results based on the INC technique indicate fast convergence rates; improvements up to 30 dB in signal-to-noise ratio and at the same time shows 65% reduction of computational power compared to conventional method

The use of scenarios and models to evaluate the future of nature values and ecosystem services in Mediterranean forests

Science and society are increasingly interested in predicting the effects of global change and socio-economic development on natural systems, to ensure maintenance of both ecosystems and human well-being. The Intergovernmental Platform on Biodiversity and Ecosystem Services has identified the combination of ecological modelling and scenario forecasting as key to improving our understanding of those effects, by evaluating the relationships and feedbacks between direct and indirect drivers of change, biodiversity, and ecosystem services. Using as case study the forests of the Mediterranean basin (complex socio-ecological systems of high social and conservation value), we reviewed the literature to assess (1) what are the modelling approaches most commonly used to predict the condition and trends of biodiversity and ecosystem services under future scenarios of global change, (2) what are the drivers of change considered in future scenarios and at what scales, and (3) what are the nature and ecosystem service indicators most commonly evaluated. Our review shows that forecasting studies make relatively little use of modelling approaches accounting for actual ecological processes and feedbacks between different socio-ecological sectors; predictions are generally made on the basis of a single (mainly climate) or a few drivers of change. In general, there is a bias in the set of nature and ecosystem service indicators assessed. In particular, cultural services and human well-being are greatly underrepresented in the literature. We argue that these shortfalls hamper our capacity to make the best use of predictive tools to inform decision-making in the context of global change.This work was supported by the Spanish Government through the INMODES project (grant number CGL2017-89999-C2-2-R), the ERA-NET FORESTERRA project INFORMED (grant number 29183), and the project Boscos Sans per a una Societat Saludable funded by Obra Social la Caixa (https://obrasociallacaixa.org/). AMO and AA were supported by Spanish Government through the “Juan de la Cierva” fellowship program (IJCI-2016-30349 and IJCI-2016-30049, respectively). JVRD was supported by the Government of Asturias and the FP7-Marie Curie-COFUND program of the European Commission (Grant “Clarín” ACA17-02)

Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO

The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients diagnosed has strained already scarce healthcare resources to track and treat Covid-19 patients in a timely and effective manner. The search for a fast and accurate way to diagnose Covid-19 has attracted the attention of many researchers. In our study, a deep learning framework for the Covid-19 diagnosis task was constructed using wavelet entropy as a feature extraction method and a feedforward neural network classifier, which was trained using an adaptive particle swarm algorithm. The model achieved an average sensitivity of 85.14% ± 2.74%, specificity of 86.76% ± 1.75%, precision of 86.57% ± 1.36%, accuracy of 85.95% ± 1.14%, and F1 score of 85.82% ± 1.30%, Matthews correlation coefficient of 71.95 ± 2.26%, and Fowlkes-Mallows Index of 85.83% ± 1.30%. Our experiments validate the usability of wavelet entropy-based feature extraction methods in the medical image domain and show the non-negligible impact of different optimisation algorithms on the models by comparing them with other models

ConvNet-CA: A Lightweight Attention-Based CNN for Brain Disease Detection

Attention-based convolutional networks have attracted great interest in recent years and achieved great success in improving representation capability of networks. However, most attention mechanisms are complicated and implemented by introducing a large number of extra parameters. In this study, we proposed a lightweight attention-based convolutional network (ConvNet-CA) that has a low computation complexity yet a high performance for brain disease detection. ConvNet-CA weights the importance of different channels in features maps and pays more attention to important channels by introducing an efficient channel attention mechanism. We evaluated ConvNet-CA on a publicly accessible benchmark dataset: Whole Brain Atlas. The brain diseases involved in this study are stroke, neoplastic disease, degenerative disease, and infectious disease. The experimental results showed that ConvNet-CA achieved highly competitive performance over state-of-the-art methods on distinguishing different types of brain diseases, with an overall multi-class classification accuracy of 94.88 ± 3.64%.</p