Author

Description

Accurate detection of brain tumors from magnetic resonance imaging (MRI) plays a vital role in clinical diagnosis and treatment planning. This paper proposes an intelligent deep learning–based framework for automated brain tumor detection using transfer learning with the InceptionV3 convolutional neural network. The proposed framework categorizes brain MRI images into four clinically relevant classes, namely glioma tumor, meningioma tumor, pituitary tumor, and no tumor. Pretrained ImageNet weights are employed to leverage rich feature representations, while the feature extraction layers of InceptionV3 are frozen to minimize overfitting and reduce computational complexity. Custom fully connected layers are integrated to enable effective multi-class classification. Furthermore, a prediction module is developed to classify unseen MRI images and provide confidence scores, facilitating real-world clinical applicability.

Keywords

Brain tumor detection, Magnetic Resonance Imaging (MRI), Deep learning, Convolutional Neural Network (CNN), InceptionV3, Transfer learning, Medical image analysis, Multi-class classification, Tumor classification, Automated diagnosis.