Predictive Analysis & Classification of Diseases in Organs using Modified-C Clustering Technique

Medical image classification is an indicative research field that gains a rising care for both research community and healthcare organizations. It helps in detection and identification of human organs as well as detection of the disease in any specified organ of human body which provide clustering and segmentation percentage with the unique identification. The objective of our proposed methodology is to detect a particular organ of human body with higher accuracy and detect any disease in the organ with higher efficiency. This polishes the enhancement of some techniques regarding data mining and proposes a classification-based approach on prominent object detection and segmentation. Our methodology consists of eight main stages: (i) data acquisition, (ii) data preprocessing, (iii) feature analysis, (iv) data classification, (v)performance evolution, (vi)clustering, (vii) segmentation and (viii) accuracy analysis. According to our object-based classification and user given random input we obtain a better result with exceptional efficiency and higher accuracyof more than 98%.