Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Classification of voice disorder in children with cochlear implantation and hearing aid using multiple classifier fusion

<p>Abstract</p> <p>Background</p> <p>Speech production and speech phonetic features gradually improve in children by obtaining audio feedback after cochlear implantation or using hearing aids. The aim of this study was to develop and evaluate automated classification of voice disorder in children with cochlear implantation and hearing aids.</p> <p>Methods</p> <p>We considered 4 disorder categories in children's voice using the following definitions:</p> <p>Level_1: Children who produce spontaneous phonation and use words spontaneously and imitatively.</p> <p>Level_2: Children, who produce spontaneous phonation, use words spontaneously and make short sentences imitatively.</p> <p>Level_3: Children, who produce spontaneous phonations, use words and arbitrary sentences spontaneously.</p> <p>Level_4: Normal children without any hearing loss background. Thirty Persian children participated in the study, including six children in each level from one to three and 12 children in level four. Voice samples of five isolated Persian words "mashin", "mar", "moosh", "gav" and "mouz" were analyzed. Four levels of the voice quality were considered, the higher the level the less significant the speech disorder. "Frame-based" and "word-based" features were extracted from voice signals. The frame-based features include intensity, fundamental frequency, formants, nasality and approximate entropy and word-based features include phase space features and wavelet coefficients. For frame-based features, hidden Markov models were used as classifiers and for word-based features, neural network was used.</p> <p>Results</p> <p>After Classifiers fusion with three methods: Majority Voting Rule, Linear Combination and Stacked fusion, the best classification rates were obtained using frame-based and word-based features with MVR rule (level 1:100%, level 2: 93.75%, level 3: 100%, level 4: 94%).</p> <p>Conclusions</p> <p>Result of this study may help speech pathologists follow up voice disorder recovery in children with cochlear implantation or hearing aid who are in the same age range.</p

Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach

Alzheimer’s disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Westerncountries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by usingautomatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selectedis based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work isfeature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. Thefeature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speechthat are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful whentraining data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters inthe feature vector in order to enhance the performance of the original system while controlling the computational cost.© 2014 Elsevier Ltd. All rights reserved

Deep Cellular Recurrent Neural Architecture for Efficient Multidimensional Time-Series Data Processing

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the-art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units