Recognition of the Gastric Molecular Image Based on Decision Tree and Discriminant Analysis Classifiers by using Discrete Fourier Transform and Features - Korkmaz, Sevcan Aytac (Taylor & Francis Inc, 2018)
This article presents the development and evaluation of a computerized decision support system (DSS), aiming to Show the feasibility and potential toward maximizing the benefits of a new algorithm by combining the machine-learning techniques which are not used in the literature for automatic recognition of the gastric images. The object of this article is fivefold: first, the features Maximally Stable Extremal Regions (MSER), Speeded Up Robust Features (SRF), and Binary Robust Invariant Scalable Keypoints (BRISK) of histopathological gastric images were analyzed. Second, the Fourier Transform (FT) was applied to these properties which were calculated to equalize the dimensions of the obtained features. Third, MS and LE size reduction methods have been applied. Fourth, the decision tree (DT) and discriminant analysis (DA) classifiers are used to classify the histopathological gastric images. Fifth, these classification results have been compared. In this article, the highest accuracy result obtained by using the SRF_FT_MS_DT method is found to be 86.66%. Fast and multimodality computerized DSS can beneficial to patients for early detection of gastric diseases. It may facilitate early diagnosis of the disease.
Keywords: Neural-Network; Classification; Diagnosis; Obesity; Disease