Background:
With the explosive growth of global data, the term Big Data describes the
enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden
patterns and secret correlations among the values. The major challenges in Big data analysis are
due to increase of volume, variety, and velocity. The capturing of images with multi-directional
views initiates the image set classification which is an attractive research study in the volumetricbased
medical image processing.
Methods:
This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief
Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed
LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent
noise from the images. Then, the application of smoothening and the normalization techniques
on the filtered image improves the intensity of the images.
Results:
The LNTP-based feature extraction categorizes the heterogeneous images into different
categories and extracts the features from each category. Based on the extracted features, the modified
DBN classifies the normal and abnormal categories in the image set finally.
Conclusion:
The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction
and DBN learning models regarding classification accuracy and runtime confirms the effectiveness
in mining applications.
Hierarchical Sparse Spectral Clustering For Image Set Classification