Automated Brain Tumor Detection Using Convolutional Neural Networks: A Comprehensive Study

Early detection of brain tumors represents an urgent need because these tumors demonstrate dangerous properties. A final brain tumour diagnosis requires invasive surgery because only biopsy tissue samples provide accurate tumor classification. Medical practitioners can use computer intelligence-assisted techniques to identify brain tumours as well as determine their types. The research used magnetic resonance brain images to develop two deep learning models together with several machine learning approaches that allowed the diagnosis of glioma and meningioma and pituitary gland tumors against healthy non-tumor brains. Doctors will achieve high accuracy in detecting early tumours by applying this CNN model which effectively separates MRI images between tumour and nontumor cases. The system depends on brain MRI images from a training database for learning with preprocessing methods including normalization and both scale and augmentation techniques to boost performance levels. The proposed forecasting model aims to improve medical professional accuracy by minimizing both wrong positive and negative results. A CNN model enables this method to perform exceptional classification of MRI images between tumour and non-tumor categories. The model receives training through brain MRI images whereby preprocessing techniques including data augmentation, scaling and normalisation help improve its performance. The proposed model works towards accurate medical forecasts by decreasing the number of false positive errors.