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Analysis of Brain Tumor Using MRI Images

Uberg Lewi, Kadry Seifedine

Annals of Computer Science and Information Systems

2022

Vol. 30

201--204

The increasing rates of deadly brain tumors inhumans correspondingly increase the need for highly experienced medical personnel for diagnosis and treatment. Therefore, to reduce the workload and the time from suspicion of disease to diagnosis, then plan for suitable treatment, there is a need to automate the initial part of the process by implementing a Computer-Aided-Disease-Diagnosis (CADD) system for brain tumor classification. By studying the types of tumors involved, how the convolutional neural network works, some of its pretrained models, and their application in brain tumor classification, the likelihood of producing a promising CADD system heavily increases. The research shows that the DenseNet121 architecture, either fully trained or using transfer learning, likely is the most appropriate candidate for the CADD system in development.