Design of ensemble classifier using Statistical Gradient and Dynamic Weight LogitBoost for malicious tumor detection

摘要

In medical field, the detection of abnormalities in breast is essential to find earlier stage of breast tumor. Conventional semi-supervised ensemble framework based on the normalized cut algorithm developed and it strongly improves the detection accuracy of the resulting images. However, further development of classification algorithm with minimum computational time and cost, several conventional methods limits the classification of tumor. In this paper, Statistical Gradient and Dynamic Weight based LogitBoost (SG-DWL) Ensemble approach is presented to improve the detection rate of malicious tumor. The key objective of SG-DWL Ensemble approach is to increase the malicious tumor performance with higher accuracy and lesser time consumption. The Statistical Gradient Boosting model is introduced as a numerical technique to improve feature set identification in test images. The proposed feature ensemble is formed by concatenating the probability density function, gradient vector and powerful algorithmic framework for feature selection with the best fit feature set is selected. Dynamic Weight based LogitBoost classifier (DW-LC) is applied for malicious tumor detection. This Dynamic Weight based LogitBoost classifier uses Hoeffding tree to achieve high malicious tumor detection rate by reducing the computational complexity involved in the classification of benign and malignant tumor. The performance of the proposed approach is evaluated by comparing it with the existing approaches, and the results improve the classification accuracy with minimum time period for malicious tumor detection.