Accurate Brain Tumour Segmentation in MRI Images using Enhanced CNN and Machine Learning Methods - Vinu, M. S.; Pasupathy, Vijayalakshmi; Senthilkumar, K. P.; Balasubramanian, K.; J., Dhanaselvam; K., Keshav Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research targets the crucial objective of brain tumor segmentation in MRI images utilising an integrated technique applying present day machine learning models. The approach starts with a rigorous preparation procedure, covering resizing, rotation, conversion, and augmentation, to optimize the dataset for further assessment. Feature extraction contains shape-primarily based, depth-based, and model-based approaches, giving an in-depth know-how of the intricate tumor features. The ensemble of machine learning to know designs contains Convolutional Neural Network (CNN), Support Vector Machine (SVM), Recurrent Neural Network (RNN), K-Nearest Neighbors (KNN), and Random Forest (RF). Training and testing on a dataset of 3290 images revealed the highest super segmentation accuracy of 9.78% for CNN main, 9.43% for SVM, 91.3% for RNN, 87.6% for KNN, and 85.4% for RF. The varied ensemble catches fantastic subtleties in brain tumor capabilities, boosting the robustness of the segmentation approach. Results illustrate the versatility of machine learning, in particular CNN, in recognising complicated patterns within scientific imaging material. The ensemble’s more than one performances stress the importance of a comprehensive method, such as outstanding machine learning to know paradigms. This evaluation gives vital information for future study in clinical image assessment in addition to enhancing mental tumour segmentation approaches. The outcomes carry incredibly fantastic promise for enhancing diagnostic accuracy, in the end extending the abilties of computerized systems in supporting doctors in the become aware of and remedy making plans of malignancies.