Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 201

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Symmetry Induction in Computational Intelligence

Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic, symmetry refers to the invariance of an object to some transformation, or set of transformations. Usually one searches for, and uses information concerning an existing symmetry within given data, structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, the question being asked is whether only existing symmetries can be useful, or whether changing reference to an intuition-based definition of symmetry over the evaluation function can also be of use. Within the context of optimization, symmetry induction as defined in this thesis will have the effect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search space. Additionally, conditions when the search space can have a symmetry induced on it are examined. The idea of a neighborhood structure then leads to the idea of opposition-based computing which aims to induce a symmetry of the evaluation function. In this context, the search space can be seen as having a symmetry imposed on it. To be useful, it is shown that an opposite map must be defined such that it equates elements of the search space which have a relatively large difference in their respective evaluations. Using this idea a general framework for employing opposition-based ideas is proposed. To show the efficacy of these ideas, the framework is applied to popular computational intelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution and neural network learning. The first example application focuses on simulated annealing, a popular Monte Carlo optimization algorithm. At a given iteration, symmetry is induced on the system by considering opposite neighbors. Using this technique, a temporary symmetry over the neighborhood region is induced. This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version. The results highlight improvements in accuracy, reliability and convergence rate. An application to image thresholding further confirms the results. Another example application, population-based incremental learning, is rooted in estimation of distribution algorithms. A major problem with these techniques is a rapid loss of diversity within the samples after a relatively low number of iterations. The opposite sample is introduced as a remedy to this problem. After proving an increased diversity, a new probability update procedure is designed. This opposition-based version of the algorithm is benchmarked using common binary optimization problems which have characteristics of deceptivity and attractive basins characteristic of difficult real world problems. Experiments reveal improvements in diversity, accuracy, reliability and convergence rate over the traditional approach. Ten instances of the traveling salesman problem and six image thresholding problems are used to further highlight the improvements. Finally, gradient-based learning for feedforward neural networks is improved using opposition-based ideas. The opposite transfer function is presented as a simple adaptive neuron which easily allows for efficiently jumping in weight space. It is shown that each possible opposite network represents a unique input-output mapping, each having an associated effect on the numerical conditioning of the network. Experiments confirm the potential of opposite networks during pre- and early training stages. A heuristic for efficiently selecting one opposite network per epoch is presented. Benchmarking focuses on common classification problems and reveals improvements in accuracy, reliability, convergence rate and generalization ability over common backpropagation variants. To further show the potential, the heuristic is applied to resilient propagation where similar improvements are also found