Comparative Study Between Q-NAS and Traditional CNNs for Brain Tumor Classification

Brain tumours caused approximately 251,329 deaths worldwide in 2020, with the primary diagnostic method for these tumours involving medical imaging. In recent years, many works and applications have observed the use of Artificial Intelligence-based models using Convolution Neural Networks (CNNs) to identify health problems using images. In our study, we searched for new architectures based on CNN using the Q-NAS algorithm. We compared its performance and number of parameters with traditional architectures such as VGG, ResNet, and MobileNet to classify types of brain tumors in MRI images. The best architecture found by Q-NAS achieved an accuracy of 92% on the test data set, with a model with less than one million parameters, which is much smaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.