Enhanced Brain Tumor Detection and Segmentation in MRI Using Deep Transfer Learning
Keywords:
low-grade gliomas (LGG ), Magnetic Resonance Imaging (MRI), convolutional neural networks (CNNs)Abstract
In the realm of medical image analysis, accurately segmenting brain tumours is crucial for precise diagnosis and treatment planning. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown significant potential in automating this task. In this research paper, we propose a method that combines a "ResNet50 model with Transfer Learning" for tumour detection and a ResUnet model with a custom loss function for segmentation. To adapt the ResNet50 model to a new dataset, we leverage transfer learning techniques. This involves initializing the model's weights with pre-trained weights from a large-scale dataset such as Imagine. By harnessing the powerful feature extraction capabilities of ResNet50 and ResUnet, the model becomes adept at identifying and segmenting brain tumours from MRI images. This approach reduces training time and improves model accuracy, particularly when working with small datasets. We evaluate our proposed method using the "The Cancer Genome Atlas (TCGA)" dataset from The Cancer Imaging Archive (TCIA). To assess its performance, we compare it against other deep learning models such as DenseNet121, K-means clustering, and VGG16 for classification and segmentation tasks. Experimental results on the testing data demonstrate that our method outperforms other deep learning networks in terms of effectiveness and efficiency. Our research paper introduces a methodology that combines ResNet50 with transfer learning for tumor detection and ResUnet with a custom loss function for segmentation in brain MRI images. The results indicate that our approach is superior to alternative deep learning models, offering improved accuracy and efficiency in brain tumor segmentation tasks
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