Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures

This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

Fusion Platform for Renal Cyst Characterization

This work concerns the field of diagnostic aids that facilitate diagnostic decisions for practitioners, especially in medical imaging. The pathology in question, in this study, is the renal cyst. The diagnostic process starts from simultaneous acquisitions of double isotope (Teechnetium-99 m and Iodine-131) scintigraphic images. Then, the platform allows the fusion of these images and the calculation of a pathological parameter that permits the characterization of the state of the dysplasic kidney by comparing it with the normal one. The final result is fusion images annotated by the pathological parameter value.

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Medical image enhancement is an essential process for superior disease diagnosis as well as for detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However, speckle noise corrupts the CT images and makes the clinical data analysis ambiguous. Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using log transform in an optimization framework. In order to achieve optimization, a well-known meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal parameter settings for log transform. The performance of the proposed technique is studied on a low contrast CT image dataset. Besides this, the results clearly show that the CS based approach has superior convergence and fitness values compared to PSO as the CS converge faster that proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness of the proposed enhancement technique.

Sparse Representations-based depth images quality assessment

The conventional 2D metrics can be used for measuring the quality of depth maps,but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality.In this paper,we propose a new full reference objective metric,called Sparse Representations-Mean Squared Error(SR-MSE),which efficiently evaluates the depth maps compression distortions.It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features.Then,it computes the mean squared error between the sparse coefficients issued from this modeling.As a benchmark of quality assessment,we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates.We compare the subjective results with the proposed and conventional objective metrics.Experimental results demonstrate that the proposed SR-MSE,compared to the conventional image quality assessment metrics,yields the highest correlated scores to the subjective ones.