Work

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

Year: 2025

Type: article

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%.

Source: Open Journal of Applied Sciences

DOI: 10.4236/ojapps.2025.154057

Authors: Gedeon Nitiema, James T. Kouawa

Institutions: Cheikh Anta Diop University, École Supérieure Multinationale des Télécommunications

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4409239034