Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification.

PURPOSE: Ovarian cancer is one of the most common gynecological cancers in women. It is difficult to accurately and objectively diagnose benign and malignant ovarian tumors using ultrasound and other tests. Hence, there is an imperative need to develop a computer-aided diagnostic (CAD) system for ovarian tumor classification in order to reduce patient anxiety and the cost of unnecessary biopsies. In this paper, we present an automatic CAD system for the detection of benign and malignant ovarian tumors using advanced image processing and data mining techniques. MATERIALS AND METHODS: In the proposed system, Hu's invariant moments, Gabor transform parameters and entropies are first extracted from the acquired ultrasound images. Significant features are then used to train a probabilistic neural network (PNN) classifier for classifying the images into benign and malignant categories. The model parameter (σ) for which the PNN classifier performs the best is identified using a genetic algorithm (GA). RESULTS: The proposed system was validated using 1300 benign images and 1300 malignant images, obtained from 10 patients with a benign disease and 10 with a malignant disease. We used 23 statistically significant (p < 0.0001) features. By evaluating the classifier using a ten-fold cross-validation technique, we were able to achieve an average classification accuracy of 99.8 %, sensitivity of 99.2 % and specificity of 99.6 % with a σ of 0.264. CONCLUSION: The proposed system is automated and hence is more objective, can be easily deployed in any computer, is fast and accurate and can act as an adjunct tool in helping physicians make a confident call about the nature of the ovarian tumor under evaluation.

Automated diagnosis of epileptic electroencephalogram using independent component analysis and discrete wavelet transform for different electroencephalogram durations.

Epilepsy is a disorder of the brain depicted by recurrent seizures. Electroencephalogram signals can be used to study the characteristics of epileptic seizures. In this study, we propose a method for the automated classification of electroencephalogram into normal, interictal and ictal classes using 6, 12, 18 and 23.6 s of data. We employed discrete wavelet transform to decompose electroencephalogram signals into frequency sub-bands. These discrete wavelet transform coefficients were then subjected to independent component analysis for reducing the data dimension. The independent component analysis features were then fed to six classifiers, namely, decision tree, K-nearest neighbor, probabilistic neural network, fuzzy, Gaussian mixture model and support vector machine to select the best classifier. We observed that the support vector machine classifier with radial basis function kernel function gave the best results with an average accuracy of 96%, sensitivity of 96% and specificity of 97% for 23.6 s of electroencephalogram data. Our results show that as the duration of the data increases, the classification accuracy increases. This proposed technique can be used as an automatic seizure monitoring software to aid the doctors in providing timely quality care for the patients suffering from epilepsy.