Brain Tumor Classification Deep Learning Model Using Neural Networks

Maquen-Niño G.L.E.; Sandoval-Juarez A.A.; Rosa R.A.V.-L.; Carrión-Barco G.; Adrianzén-Olano I.; Vega-Huerta H.; De-La-Cruz-VdV P. · International journal of online and biomedical engineering (2023) · DOI: 10.3991/ijoe.v19i09.38819

Resumen

The timely diagnosis of brain tumors is currently a complicated task. The objective was to build an image classification model to detect the existence or not of brain tumors by adding a classification header to a ResNet-50 architecture. The CRISP-DM methodology was used for data mining. A dataset of 3847 brain MRI images was used, 2770 images for training, 500 for validation, and 577 for testing. The images were resized to a 256 × 256 scale and then a data generator is created that is responsible for dividing pixels by 255. The training was performed and then the evaluation process was carried out, obtaining an accuracy percentage of 92% and a precision of 94% in the evaluation process. It is concluded that the proposed CNN model composed of a head with a ResNet50 architecture and a seven-layer convolutional network achieves adequate accuracy, becoming an efficient and complementary proposal to other models developed in previous works.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional Pedro Ruiz Gallo; Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas