A Transfer Learning-Based Approach for Brain Tumor Classification

This project focuses on the classification of brain tumors using deep learning techniques, specifically leveraging the Inception v3 network. The dataset consists of brain tumor images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. The process begins with pre-processing steps such as image resizing, normalization, rotation, scaling, cropping, flipping, and noise addition to enhance the dataset's variability and improve the model's robustness. Inception v3 is employed for its strong performance in image classification tasks, with the architecture involving multiple convolutional layers and pre-trained weights for efficient feature extraction. After fine-tuning on the brain tumor dataset, the network undergoes training with specialized options to classify tumors with high accuracy. This approach integrates both image processing techniques and advanced deep learning frameworks to achieve precise tumor categorization, facilitating improved diagnosis and treatment planning. The model is evaluated to ensure reliable performance in distinguishing between glioma, meningioma, pituitary tumors, and non-tumorous conditions.

Keywords: Brain tumor Dataset, Pre-Processing, Convolutional Neural Networks, Deep learning, Inception V3, Classification, Accuracy.