Diagnosis of Brain Tumors Using a Convolutional Neural Network

Percy DelaCruz-VdV, Dayanne Cadenillas-Rivera, Hugo Vega-Huerta, Ernesto Cancho-Rodriguez, María Elizabeth Puelles Bulnes, Gisella Luisa Elena Maquen-Niño, Jorge Pantoja-Collantes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The purpose of this research is to support the diagnosis of brain tumors in adult patients. To obtain greater accuracy in the detection of the diagnosis, the following input variables were considered: type; class; grade; location and size of the brain tumor. To achieve the proposed objective, a convolutional neural network composed of three levels of layers was implemented: (a) the input layer, which captures the images and applies a filter or kernel that returns a map of the characteristics of the original image, thus reducing the size of the parameters; (b) the reduction or pooling layer whose function is to reduce the spatial dimensions of the input volume; and (c) a connected classifier layer: this classifier layer will have as many neurons as the number of classes to be predicted. A total of 2870 images were used to support the implementation and training of the convolutional neural network model and thus identify whether the patient's brain was healthy or had a brain tumor, for this diagnosis an image of a real case of a patient with a brain tumor was taken, and the result was the identification of the type of tumor “pituitary”, i.e., the convolutional neural network effectively diagnosed the brain tumor. The results obtained indicate that the trained model was effective and efficient. To validate the effectiveness of the algorithm, the confusion matrix was used, which yielded an accuracy of 0.9059.

Original languageEnglish
Title of host publicationPerspectives and Trends in Education and Technology - Selected Papers from ICITED 2023
EditorsAnabela Mesquita, António Abreu, João Vidal Carvalho, Cleuciliz Santana, Cristina Helena de Mello
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789819954131
StatePublished - 2023
EventInternational Conference in Information Technology and Education, ICITED 23 - Manaus, Brazil
Duration: 29 Jun 202330 Jun 2023

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


ConferenceInternational Conference in Information Technology and Education, ICITED 23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.


  • Accuracy
  • Confusion matrix
  • Convolutional neural network
  • Diagnosis of brain tumor


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