A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet

A brain tumor is a disorder caused by the growth of abnormal cells. The survival rate patient affected with difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role determining site; however, manual detection time-consuming challenging procedure that cause some errors results. adoption computer-assisted approaches help overcoming these constraints. With advancement artificial intelligence, deep learning (DL) models being used medical imaging diagnose using MR images. In this study, convolutional neural network (CNN) EfficientNet-B0 base model fine-tuned our proposed layers efficiently classify detect image enhancement techniques applying filters enhance quality Data augmentation methods applied increase data samples for better training model. results show state-of-the-art outperforms other CNN achieving highest classification accuracy, precision, recall, area under curve values surpassing models, overall accuracy 98.87% terms detection. Other DL algorithms such as VGG16, InceptionV3, Xception, ResNet50, InceptionResNetV2 comparative analysis.