On-Device Brain Tumor Classification from MR Images Using Smartphone

Correct and rapid classification of brain tumor types is crucial for the patient's treatment plans. This study aims to create a deep learning-based mobile application that leverages on-device AI capabilities to classify brain tumors. For this reason, first, a series of preprocessing steps are applied to MR images. Then, convolutional neural network , ViT, and MobileViT models are trained for this task. Also, pretrained VGG16, ResNet152V2, InceptionV3, InceptionResNetV2, and MobileNetV2 models are retrained for the brain tumor classification task with the transfer learning method. Using the publicly available "Brain Tumor MRI Dataset," the model performances are evaluated, and test results are compared. MobileViT shows the best performance in terms of balance between inference time and success rate. Thus, the TensorFlow model of MobileViT is converted to the TensorFlow Lite model and integrated into the mobile application. The mobile application is developed using the Flutter framework. The application has been evaluated on two different devices, and 298.98 and 317.50 ms average inference times have been observed. The proposed system shows that rapid and effective brain tumor classification can be performed by integrating deep learning into the mobile application. This system can assist experts in the decision-making process.