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Musallam, A.S., Sherif, A.S. and Hussein, M.K. (2022) A Novel Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging. IEEE Access, 10, 2775-2782.
https://doi.org/10.1109/ACCESS.2022.3140289

has been cited by the following article:

• TITLE: Impact of Data Augmentation Technique on Automatic Brain Tumor Detection Using ResNet CNN

  AUTHORS: Gédéon Nitiema, James T. Kouawa

  KEYWORDS: MRI, ResNet, CNN, Augmentation, Brain Tumor

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.4, April 7, 2025

  ABSTRACT: The research article studies the impact of data augmentation, particularly 60˚ rotation, on the performance of a ResNet convolutional neural network (CNN) for brain tumor detection using MRI images. A limited Kaggle dataset was used, supplemented with augmented images. The study compares model performance with and without augmentation, analyzing metrics such as accuracy, precision, recall, F1 score, and observing training and validation curves. The results indicate that data augmentation significantly improved performance, increasing accuracy from 63% to 90%.