An Approach in Brain Tumor Classification: The Development of a New Convolutional Neural Network Model - Felipe, Caio dos Santos; Alva, Thatiane Alves Pianoschi; Winck, Ana Trindade; Becker, Carla Diniz Lopes (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e Computacional, 2023)
Brain tumor diagnosis is a complex problem that requires specialized skills and knowledge. Manual analysis is often time-consuming, and there can be high subjectivity in interpreting results. Convolutional neural networks (CNNs) have emerged as a promising solution for automatically classifying brain tumors from magnetic resonance images (MRI). CNNs are a type of neural network that can automatically learn and extract relevant features from images, making them particularly suited to this task when applied in deep learning algorithms. The use of CNNs for brain tumor diagnosis has been widely explored in the literature, with many studies reporting promising results. By leveraging datasets of labeled MRI, CNNs can learn to accurately detect and classify different types of brain tumors, including gliomas, meningiomas, and pituitary adenomas. These models have been shown to outperform traditional machine-learning algorithms and even human experts in some cases. This article presents a CNN model designed to identify and classify brain tumors from MRI. The model was trained on a large dataset of MRI, and its performance was evaluated on an independent test set. The model achieved an accuracy of 99% considering all validation steps and outperformed state-of-the-art methods for brain tumor classification. When considering individual classes, the accuracy percentages were 100%, 98%, 99%, and 99% for glioma, meningioma, notumor, and pituitary, respectively. The development of accurate and efficient methods for brain tumor diagnosis is critical for improving patient outcomes and reducing healthcare costs. This article can advance our understanding of leveraging these powerful algorithms best to solve real-world healthcare problems by contributing to the growing literature on deep learning for medical image analysis.