The development of medical images has facilitated the diagnosis of brain diseases. The diagnosis of brain medical images has the characteristics of uneven distribution of categories and different costs of misclassification. Therefore, traditional classification algorithms are used in clinically confirmed MRI brains. When a medical image is used as a training set to construct a classification model, the classification effect is poor and it is easy to be insensitive to the positive class, which makes it difficult for the brain disease auxiliary diagnosis system to have high accuracy and weak generalization ability. The research purpose of this paper is to study the assistant diagnosis system of brain diseases based on the uneven distribution of medical image categories. In order to improve the performance of the assistant diagnosis system of brain diseases, this paper designs a cost-sensitive probabilistic neural network CS-PNN brain by introducing cost-sensitive this system is an auxiliary diagnosis system for diseases, and the reliability of the system is verified by experiments. It can be known from experiments that the cost-sensitive probabilistic neural network CS-PNN assisted diagnosis system for brain diseases designed in this paper increases with the cost of positive misclassifications and negative misclassifications, and the classification accuracy rate of CS-PNN continues to increase. (01) = 4 achieves the best classification performance of 97%. The research in this article provides new ideas for solving the problems of uneven distribution of categories and misclassification costs in MRI brain medical images, so as to develop a brain disease auxiliary diagnosis system with stronger generalization ability, thereby improving the diagnosis of brain tumors. Accuracy and reduce missed diagnosis.

Junxiu Wang. Assisted Diagnosis System for Brain Diseases with Imbalanced Category Distribution Based on Medical Images. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 6: 53-66. https://doi.org/10.25236/IJFET.2021.030606.

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