Early Detection of Brain Tumors Using Artificial Intelligence

Effective early brain tumor detection is still a major medical challenge. Artificial intelligence (AI), particularly convolutional neural networks (CNNs), offers promising solutions in this field. This study uses a dataset of 16,400 images from Magnetic Resonance Imaging (MRI) to detect brain tumors. There are four different classes in the dataset: "no tumor," "glioma tumor," "meningioma tumor," and "pituitary tumor." The suggested CNN model was created with an optimized model architecture and an effective training process. Compared to previous pretrained models, the model was trained on a large-scale dataset with fewer layers, which allowed it to maintain high accuracy while being quicker and requiring fewer resources. A lightweight yet effective model was made possible by important architectural elements like dropout for overfitting prevention and six convolutional layers. Techniques for data augmentation and optimization were used to improve the model's generalization even more. This CNN can be used even in environments with limited resources because, in contrast to other pretrained models, it efficiently used a large dataset while keeping its computational complexity manageable. The CNN-based classification model demonstrated class-specific performance metrics: 'glioma tumor' (94%), 'meningioma tumor' (93%), 'no tumor' (99.99%), and 'pituitary tumor' (99%), achieving impressive accuracies. With an overall accuracy of 97.66%, it outperformed earlier models in the field. In contrast to earlier studies, this study emphasizes the promise of AI, especially CNNs, to transform early brain tumor detection by handling large datasets with fewer layers than many pretrained models while using less time and computational power.