Utilizing Advanced Deep Learning Techniques for Brain Tumor Classification

Brain tumors, both benign and malignant, pose a substantial hazard to human health, and needing accurate and prompt diagnosis for successful treatment. The goal of this work is to improve brain tumor detection accuracy and effectiveness of categorization through the implementation of an advanced technique. The proposed method leverages the ResNet50 deep neural network, renowned for its proficiency with extensive datasets. By utilizing the pre-trained ResNet50 model from the ImageNet dataset and employing the Kaggle dataset for testing, we conducted comprehensive data preparation, including partitioning into distinct training and validation sets. Over 20,000 categories and over 14 million records make up the ImageNet collection or classes. This vast dataset has been instrumental in training various deep learning models, including ResNet50, to recognize and classify objects accurately. In our suggested method, comprehensive data preparation, including partitioning into distinct training and validation sets, was conducted. Data augmentation techniques were employed to enhance model robustness, resulting in impressive accuracy levels of up to 98% in brain tumor categorization. Our research sets the stage for more precise clinical diagnoses and improved treatment strategies in the realm of brain tumor classification, thereby potentially saving lives.