A Hybrid Approach to Brain Tumor Detection: Combining Deep Convolutional Networks with Traditional Image Processing Methods for Enhanced MRI Classification
Year: 2024
Type: article
Abstract: Brain tumour detection is a critical medical procedure where early and accurate diagnosis significantly improves patient outcomes. This study explores the application of deep learning models, specifically VGG19 and InceptionV3, for detecting brain tumours in MRI images. We fine-tuned both models using transfer learning and evaluated them on a dataset of MRI scans. InceptionV3 achieved 100% validation accuracy, while VGG19 achieved 95%, demonstrating their high efficacy in medical image classification. In addition to deep learning, the study integrates insights from traditional image processing techniques, such as edge detection and probabilistic neural networks (PNNs), highlighting how combining deep learning with traditional methods can enhance image preprocessing and quality. The results underscore that while deep learning models are highly effective in brain tumour classification, traditional techniques still play a vital role in optimizing the overall detection process.
Authors: Muhammad Maruf Billah, Abdullah Al Rakib, Md Shakawat Hossain, Mst Kamrun Nahar Borsha, Nazmul Nahid, Md Nahidul Islam
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4404380803