International Journal of Applied Mathematics and Machine LearningAuthor's: Omar Villalpando-Vargas, Aron Hernandez-Trinidad, Teodoro Cordova-Fraga and Rafael Guzman-Cabrera
Pages: [83] - [97]
Received Date: April 10, 2023
Submitted by: Professor Jianqiang Gao
DOI: http://dx.doi.org/10.18642/ijamml_7100122276
Artificial Intelligence through advanced algorithms, such as deep learning, has recently been applied in medical imaging, representing an emerging area in the classification of medical pathologies. Deep learning has been widely applied in medical imaging to help automate and improve medical image analysis. Medical imaging techniques such as MRIs, CT scans, and X-rays produce large amounts of data that can be difficult for human experts to accurately interpret and analyze. Deep learning algorithms can be trained to recognize patterns in medical images, identify abnormal features, and make predictions about patient outcomes. The present research work exposes an automatic classification model to detect brain tumors in brain magnetic resonance images (MRI). The data set is found in the Kaggle repository, which consists of 253 MRI images: 155 with brain tumors and 98 without. The proposed model can classify brain tumor MRI images with 91% accuracy. Therefore, the model represents an auxiliary tool to existing conventional methods for the diagnosis of brain tumors.
deep learning, MRI images, automatic classification model.
