Alzheimer image registration using hybrid random forest and deep regression network algorithm

Ramakoteswararao Siddabathuni, Sivagurunathan Palanivel, Godavarthi Lakshmi Narasimha Murthy


Image registration involves superimposing images (two or more) of similar background obtained at various periods of time, at different angles, and/or with various detectors. Geometrical alignment of two scans, reference image as well as capture image. The current dissimilarity between images is because of distinct image conditions. Image registration is difficult step in image analysis works on change detection, image fusion as well as
multi-channel images recovery to obtain concluded data from integration of different sources. In this analysis image registration using hybrid random forest (RF) and deep regression network algorithm for magnetic resonance imaging (MRI) applications is implemented. The Alzheimer’s disease neuroimaging initiative (ADNI) database provided by the dataset utilised in this implementation. From results it can observe that compared with individual random of forest, Hybrid RF and deep regression network algorithm improves the accuracy, precision and F1-score in effective way.


Classification algorithm; Deep regression network; Hybrid random forest; Image registration; Random forest

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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