An Automated Medical Diagnosis System for Neoplasm Medical Image Classification Using Supervised and Unsupervised Techniques
Sreedhar Kumar Seetharaman, Basant Kumar, Manjunath Chikkanjinappa Rajanna, Syed Thouheed Ahmed
Abstract
In this research, an improved automated medical prediction system, namely, the Neoplasm Medical Image Classification System (NMICS), is proposed. The proposed NMICS aims to robotically identify whether the given test magnetic resonance image (MRI) belongs to the tumor group or the non-tumor group using machine learning techniques. The proposed NMICS is divided into two stages, namely, the Train Medical Image Model (TMIM) and the Medical Image Prediction Stage (MIPS), respectively. In the TMIM stage, the NMICS performs various distinct operations including improving input medical image data set quality and consistency through standard arithmetic operations; extracting specific features (edge) from every individual medical image in the input medical image set using the CNN method; and separating the feature vector set of the input medical image set into two distinct clusters, namely, tumor and non-tumor, respectively, using the unsupervised k-means clustering technique. In the MIPS stage, the proposed (NMIC) system performs the same types of operations, including preprocessing and feature extraction, on the test medical image samples. Next, the NMICS maps and classifies the feature vector of the test medical image sample against trained medical image data set clusters using a KNN classifier. The investigation results show that the NMICS is well-suited to diagnosing whether the given medical image is grouped into the neoplasm category or the non-neoplasm group.