Browsing by Author "Pulkkinen, Jenni"

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Citation - WoS: 33
Citation - Scopus: 37
Classification and Retrieval on Macroinvertebrate Image Databases
(Pergamon-Elsevier Science Ltd, 2011) Kiranyaz, Serkan; İnce, Türker; Pulkkinen, Jenni; Gabbouj, Moncef; Arje, Johanna; Karkkainen, Salme; Tirronen, Ville
Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing classification and data retrieval that are instrumental when processing large macroinvertebrate image datasets. To accomplish this for routine biomonitoring, in this paper we shall investigate the feasibility of automated river macroinvertebrate classification and retrieval with high precision. Besides the state-of-the-art classifiers such as Support Vector Machines (SVMs) and Bayesian Classifiers (BCs), the focus is particularly drawn on feed-forward artificial neural networks (ANNs), namely multilayer perceptrons (MLPs) and radial basis function networks (RBFNs). Since both ANN types have been proclaimed superior by different investigations even for the same benchmark problems, we shall first show that the main reason for this ambiguity lies in the static and rather poor comparison methodologies applied in most earlier works. Especially the most common drawback occurs due to the limited evaluation of the ANN performances over just one or few network architecture(s). Therefore, in this study, an extensive evaluation of each classifier performance over an ANN architecture space is performed. The best classifier among all, which is trained over a dataset of river macroinvertebrate specimens, is then used in the MUVIS framework for the efficient search and retrieval of particular macroinvertebrate peculiars. Classification and retrieval results present high accuracy and can match an experts' ability for taxonomic identification. (C) 2011 Elsevier Ltd. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 39
Evaluation of Global and Local Training Techniques Over Feed-Forward Neural Network Architecture Spaces for Computer-Aided Medical Diagnosis
(Pergamon-Elsevier Science Ltd, 2010) İnce, Türker; Kiranyaz, Serkan; Pulkkinen, Jenni; Gabbouj, Moncef
In this paper, we investigate the performance of global vs. local techniques applied to the training of neural network classifiers for solving medical diagnosis problems. The presented methodology of the investigation involves systematic and exhaustive evaluation of the classifier performance over a neural network architecture space and with respect to training depth for a particular problem. In this study, the architecture space is defined over feed-forward, fully-connected artificial neural networks (ANNs) which have been widely used in computer-aided decision support systems in medical domain, and for which two popular neural network training methods are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). Both training techniques are compared in terms of classification performance over three medical diagnosis problems (breast cancer, heart disease, and diabetes) from Pro-ben1 benchmark dataset and computational and architectural analysis are performed for an extensive assessment. The results clearly demonstrate that it is not possible to compare and evaluate the performance of the two algorithms over a single network and with a fixed set of training parameters, as most of the earlier work in this field has been carried out, since training and test classification performances vary significantly and depend directly on the network architecture, the training depth and method used and the available dataset. We, therefore, show that an extensive evaluation method such as the one proposed in this paper is basically needed to obtain a reliable and detailed performance assessment, in that, we can conclude that the PSO algorithm has usually a better generalization ability across the architecture space whereas BP can occasionally provide better training and/or test classification performance for some network configurations. Furthermore, we can in general say that the PSO, as a global training algorithm, is capable of achieving minimum test classification errors regardless of the training depth, i.e. shallow or deep, and its average classification performance shows less variations with respect to network architecture. In terms of computational complexity, BP is in general superior to PSO for the entire architecture space used. (C) 2010 Elsevier Ltd. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 21
Network of Evolutionary Binary Classifiers for Classification and Retrieval in Macroinvertebrate Databases
(IEEE, 2010) Kiranyaz, Serkan; Gabbouj, Moncef; Pulkkinen, Jenni; İnce, Türker; Meissner, Kristian
In this paper, we focus on advanced classification and data retrieval schemes that are instrumental when processing large taxonomical image datasets. With large number of classes, classification and an efficient retrieval of a particular benthic macroinvertebrate image within a dataset will surely pose a severe problem. To address this, we propose a novel network of evolutionary binary classifiers, which is scalable, dynamically adaptable and highly accurate for the classification and retrieval of large biological species-image datasets. The classification and retrieval results for the macroinvertebrate test data attain taxonomic accuracy that equals and even surpasses that of an average expert. Our findings are encouraging for aquatic biomonitoring where cost intensity of sample analysis currently poses a bottleneck for routine biomonitoring.
Citation - WoS: 42
Citation - Scopus: 50
Personalized Long-Term Ecg Classification: a Systematic Approach
(Pergamon-Elsevier Science Ltd, 2011) Kiranyaz, Serkan; İnce, Türker; Pulkkinen, Jenni; Gabbouj, Moncef
This paper presents a personalized long-term electrocardiogram (ECG) classification framework, which addresses the problem within a long-term ECG signal, known as Halter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Halter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore, the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so-called master key-beats) each of which is automatically extracted from a time frame of homogeneous (similar) beats. We tested the system on a benchmark database where beats of each Halter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and thus we used exhaustive K-means clustering in order to find out (near-) optimal number of key-beats as well as the master key-beats. The classification process produced results that were consistent with the manual labels with over 99% average accuracy, which basically shows the efficiency and the robustness of the proposed system over massive data (feature) collections in high dimensions. (C) 2010 Elsevier Ltd. All rights reserved.