Abstract :
Due to the high level exposure of biomedical image analysis,Medical image mining hasbecome one of the well-establishedresearch area(s) of machine learning. AI (Artificial Intelligence) techniques have been vastly used to solve the complex classification problems of thyroid cancer. Since the persistence of copycat chromatin properties and unavailability of nuclei measurement techniques, itis really problem for doctors to determinethe initial phases of nuclei enlargement and to assess the early changes of chromatin distribution. For example involvement of multiple transparent overlappingof nuclei may become the cause of confusion to infer the growth pattern of nuclei variations.Un-decidable nucleieccentric properties may become one of the leading causesfor misdiagnosisinAnaplast cancers. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Decision Support System for Anaplast Thyroid Cancer” and it proposes a medical data preparation algorithm AD (Analpast_Cancers) which helps to select the appropriate features of Anaplastcancers such as (1) enlargement of nuclei, (2) persistence of irregularity in nuclei and existence of hyper chromatin. Proposed methodology comprises over four major layers, thefirst layer deals with the noise reduction, detection of nuclei edges and object clusters. The Second layer selects the features of object of interest such as nuclei enlargement, irregularity and hyper chromatin. The Third layer constructs the decision model to extract the hidden patterns of disease associated variables and the final layer evaluates the performance evaluation by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 97.2% with 10-k fold cross validation.
Keyword :
biomedical image;algorithm;classification;dicision support system