Comparative Evaluation for Brain Tumor Detection Using Inception-V3 Architecture

Abstract: Over the last decade, researchers have been focusing on magnetic resonance imaging (MRI) to detect brain tumors. However, existing methods that involve medical image feature extraction is not sufficient to solve this issue. To tackle this problem, a new model has been proposed, employing the Inception-v3 convolutional neural network. By extracting and categorising various features, this model can help identify s brain tumors earlier. The proposed model is built on Inception-v3 and utilizes loss functions and the Adam Optimizer to optimize its hyperparameters. It also employs a softmax classifier to classify the images into different classes. The results indicate that the Inception-v3 algorithm achieved an impressive training data accuracy of 99.02% and a validation data accuracy of 89%.