M. Cevdet Ince

An expert system for detection of breast cancer based on association rules and neural network

This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR+NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.

Wavelet packet neural networks for texture classification

Abstract Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. This paper describes the usage of wavelet packet neural networks (WPNN) for texture classification problem. The proposed schema composed of a wavelet packet feature extractor and a multi-layer perceptron classifier. Entropy and energy features are integrated wavelet feature extractor. The performed experimental studies show the effectiveness of the WPNN structure. The overall success rate is about 95%.

A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases

In this paper, a new feature selection method based on Association Rules (AR) and Neural Network (NN) is presented for the diagnosis of erythemato-squamous diseases. AR is used for reducing the dimension of erythemato-squamous diseases dataset and NN is used for efficient classification. The proposed AR+NN system performance is compared with that of other feature selection algorithms+NN. The dimension of input feature space is reduced from thirty four to twenty four by using AR. In test stage, 3-fold cross validation method is applied to the erythemato-squamous diseases dataset to evaluate the proposed system performances. The correct classification rate of proposed system is 98.61%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.

Shape feature encoding via Fisher Vector for efficient fall detection in depth-videos

FV can be considered as a generalization of the BoW. In other words, BoW is a particular case of the FV. The additional gradients improve the FVs performance greatly.Smaller codebooks can be used to construct the FV, which yields lower computational cost.FV performs well even with simple linear classifiers. Elderly people, who are living alone, are at great risk if a fall event occurred. Thus, automatic fall detection systems are in demand. Some of the early automatic fall detection systems such as wearable devices has a high cost and may cause inconvenience to the daily lives of the elderly people. In this paper, an improved depth-based fall detection system is presented. Our approach uses shape based fall characterization and a Support Vector Machines (SVM) classifier to classify falls from other daily actions. Shape based fall characterization is carried out with Curvature Scale Space (CSS) features and Fisher Vector (FV) encoding. FV encoding is used because it has several advantages against the Bag-of-Words (BoW) model. FV representation is robust and performs well even with simple linear classifiers. Extensive experiments on SDUFall dataset, which contains five daily activities and intentional falls from 20 subjects, show that encoding CSS features with FV encoding and a SVM classifier can achieve an up to 88.83% fall detection accuracy with a single depth camera. This classification rate is 2% more accurate than the compared approach. Moreover, an overall 64.67% accuracy is obtained for 6-class action recognition, which is about 10% more accurate than the compared approach.

Wavelet domain association rules for efficient texture classification

The wavelet domain association rules method is proposed for efficient texture characterization. The concept of association rules to capture the frequently occurring local intensity variation in textures. The frequency of occurrence of these local patterns within a region is used as texture features. Since texture is basically a multi-scale phenomenon, multi-resolution approaches such as wavelets, are expected to perform efficiently for texture analysis. Thus, this study proposes a new algorithm which uses the wavelet domain association rules for texture classification. Essentially, this work is an extension version of an early work of the Rushing et al. [10,11], where the generation of intensity domain association rules generation was proposed for efficient texture characterization. The wavelet domain and the intensity domain (gray scale) association rules were generated for performance comparison purposes. As a result, Rushing et al. [10,11] demonstrated that intensity domain association rules performs much more accurate results than those of the methods which were compared in the Rushing et al. work. Moreover, the performed experimental studies showed the effectiveness of the wavelet domain association rules than the intensity domain association rules for texture classification problem. The overall success rate is about 97%.

Unsupervised image segmentation using markov random fields

In this study, we carried out an unsupervised gray level image segmentation based on Markov Random Fields (MRF) model. First, we use the Expectation Maximization (EM) algorithm to estimate the distribution of the input image and the number of the components is automatically determined by the Minimum Message Length (MML) algorithm. Then the segmentation is done by the Iterated Conditional Modes (ICM) algorithm. For testing the segmentation performance, we use both artificial images and real images. The experimental results are satisfactory.