Edge Boost Curve Transform and Modified ReliefF Algorithm for Communicable and Non Communicable Disease Detection Using Pathology Images

Abstract

In this paper, a five phase model is proposed for early detection of communicable and non-communicable diseases like Haemoprotozoan and breast cancer using pathology images. At first, color normalization technique isutilized to improve the visual quality of the collected histology images. Next, edge boost curve transform is employed to segment nuclei and non-nuclei cells from the enhanced images. The developed segmentation methodology delivers good results in overlapped database. Further, the segmented image is converted into one dimensional vectors and then modified reliefF algorithm is applied to choose the active feature vectors to achieve better classification. Finally, deep neural network is accomplished to classify the Haemoprotozoan images as anaplasmosis, babesiosis and theileriosis, and breast images as malignant or benign. From the experimental result, the proposed model; modified reliefF-deep neural network obtained maximum classification accuracy of 97.6% in Haemoprotozoan disease detection and 95.94% in breast cancer detection, which are better related to other comparative techniques like Random Forest, Multi Support Vector Machine and K-Nearest Neighbor