Enhanced Prediction of Brain Tumor Growth through Advanced Machine Learning Techniques
Abstract
Brain tumors pose significant health risks, requiring effective treatment planning for patient survival. Medical imaging techniques like MRI assist in tumor diagnosis, but manual classification is hindered by data volume. Automatic classification schemes, such as CNN, are crucial for accurate detection and treatment.
Keywords
Download Options
Introduction
The brain consists of billions of cells, some of which can form tumors. These tumors are categorized as low grade (benign) or high grade (malignant). MRI imaging is crucial for tumor detection and treatment planning due to its detailed information about brain structure. Machine learning techniques like Neural Networks and Support Vector Machines have been commonly used for tumor detection, but recently, Deep Learning models have gained traction due to their ability to represent complex relationships effectively.
Conclusion
The research aims to create an accurate and efficient brain tumor classification system. Traditional methods like Fuzzy C Means with SVM and DNN show low accuracy and high computation time. To improve this, a convolutional neural network (CNN) approach is proposed, enhancing accuracy and reducing computation time. The CNN is implemented using Python, leveraging the ImageNet database for classification. Pre-trained models are utilized, with training focused on the final layer. Features extracted include raw pixel values and dimensions. Gradient descent-based loss function ensures high accuracy, with training and validation showing promising results.