Application of Inductive Learning in Human Brain CT Image Recognition

This paper presented three ways utilized to do head CT (Computed Tomography) image Computer Aided Diagnosis and their performance comparison, and analysis of the performance comparison. In this present work, we collected 116 normal pieces of head CT image and 96 abnormal ones, and utilized two ways to do feature extraction, and applied the ways of decision tree, RBFNN and outlier detection in classification. At the same time, it was implied in this paper that one question formulation could lead to different question solutions by utilizing different methods. The question formulation is the base of measure which we make use of. For this point of view, the question formulation is more important than question solution. Meanwhile, as future work, we will try to find different formulations of this question by making different question solutions as guidance. So we can achieve more conclusions of the question, and can select optimal solution in greater scope.