Determination of accuracy and probability in the analysis of large-scale biomedical data

Abstract

The paper below introduces an image classification workflow concept for biomedical purposes and present the process of determination of accuracy and probabilities in the analysis of large-scale biomedical data. The workflow is based on image features extraction through deep network embedding. The distance computation in the workflow is accomplished using Cosine distance. The experiments are conducted on the base of the Logistic Regression, Random Forest and Naïve Bayes and are directed on accuracy and probability in the analysis of large-scale data. The performed analysis shows the advantage of the Logistic Regression results which are considered to be the most reliable compared to the results generated through the Random Forest and Naïve Bayes.