Brain Tumor Classification Deep Learning Model Using Neural Networks
DOI:
https://doi.org/10.3991/ijoe.v19i09.38819Keywords:
deep learning, brain tumors, convolutional neural networks, classification models, Resnet50Abstract
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.
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Copyright (c) 2023 Gisella Luisa Elena Maquen-Niño, Ariana Ayelen Sandoval-Juarez, Robinson Andres Veliz-La Rosa, Gilberto Carrión-Barco, Mg. Ivan Adrianzén-Olano, Dr. Hugo Vega-Huerta, Percy De-La-Cruz-VdV
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