Classification Method For Diagnosis Research Articles

High accuracy multilayer autoencoder trained classification method for diagnosis of Parkinson’s disease using vocal signals

Dysarthria and dysphonia are two commonly occurring disorders of speech in Parkinson’s disease patient, which are observed in approximately 90 % of the disease cases. It has been reported that these disorders give early sign of Parkinson’s disease. Hence, effective development of diagnostic tools for detecting early biomarkers can help in controlling the symptoms of disease. In this paper, we have proposed a classification model using Multi- layer Autoencoder for feature space reduction. Comparisons among 6 types of classification methods with various feature space sizes were conducted to find out the best performing classification algorithm and feature space size based on accuracy, specificity, and sensitivity. Mel Frequency Cepstral method was used to extract set of features from voice signals of both healthy group and unhealthy group. The model showed promising results in classification when combined with Multi-Layer Autoencoder and SVM classifier.

Predicting Physico-Chemical Characteristics of Brain Tumour

This research proposes a series of algorithms that work for improved Brain Tumor identification and classification. The Brain Tumor study based on the MRI image will effectively resolve the classification method for diagnosis of brain tumors. There are three stages: Extraction of features, Reduction of features and classification. Extraction function and reduction of functionality used for two algorithms. The extracted characteristics are Mean, Standard deviation, Curtosis, Skewness, Entropy Contrast, Variance, Smoothness, Correlation and Power. The result is then supplied to Support Vector Machine (SVM) for the Benign or Malignant classification of tumours.

Intelligent diagnosis and maintenance management

This paper presents a few of the research and development activities performed at MARINTEK (Norwegian Marine Technology Research Institute AS, a part of the SINTEF Group) in the field of condition monitoring, fault diagnosis and maintenance planning during the last 10 years. The activities comprise the development of a rule-based diagnosis system, experiments with learning-based pattern classification methods in diagnosis and prototyping of decision support systems for maintenance planning.