Brain tumor classification by CNN

In this paper, we proposed a disease classification method for brain images using CNN. The research dataset collects images of normal, blastoma, meningioma, adenoma, and glioblastoma by brain tumor disease from journals such as NEJM and AuntMinnie and files them in Exam_Brain.Zip. As a result of CNN processing the images for each file folder in the Exam_Brain.zip file 10 times, the AUC was found to be 0.8825. As a result of the experiment, the brain disease classification accuracy of brain magnetic resonance imaging was found to be 88.25%. These results indicate that the results of this study can be used to classify other diseases once the data set is established, and can also be used to classify objects in other industries.This study has the following limitations. A challenge in automatically classifying diseases is the vastness and integrity of the data sets. If the data is not prepared, the accuracy of classification will be problematic. Additionally, in the medical field, a doctor's decision-making (diagnosis) is not limited to image data but is made based on comprehensive data, and there is a limitation in that medical decisions cannot be made by machines without the intervention of a doctor. Determining the decision threshold in AI decision-making problems is also an important limitation. This is because setting reference points for sensitivity and specificity is not the domain of AI experts, but the domain of experts in the field. Therefore, field medical staff must participate in the process of developing AI in medical settings. Future research tasks include developing applications that improve performance by developing the input stage of the program, and continuing research in connection with medical big data servers.

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