Deep Learning Approaches for Brain Tumour Detection Using VGG-16 Architecture

Variety and complexity in MRI appearances, combined with their hidden, silent location make brain tumours an insurmountable challenge to medical diagnosis. Only when we can obtain timely diagnosis does the disease develop. Therefore, diagnosis results must be no longer late at third-stage. Baded on the strong feature-extracting abilities of VGG-16, this paper investigates how it performs (CNN) in discovering brain tumours. The model was trained with a dataset of 3,253 MRI pictures both positive for tumour examples and examples negative for tumour. By applying the technique of transfer learning combined with fine-tuning and data augmentation as well as regularization methods to reduce overfitting while enhancing overall performance, the model reached an acceptable level. The VGG-16 model boasted an accuracy rate of 94%. With precision, recall and F1 rankings each over 93%, it illustrates reliable ability to distinguish tumour versus non-tumour cases. The high degree of sensitivity that the model displays ensures that most positive instances are correctly identified, thus making it a valuable tool for assisting medical personnel in diagnosing brain tumours. In the model's case, while performed well, potential for improvement lies in employing advanced architectures and acquiring larger, more varied data sets. This study demonstrates that deep learning models such as VGG-16 can be used to help medical image analysis and improve diagnostic accuracy.