Optimizing Brain Tumour Classification Models through Advanced Image Segmentation Techniques

Abstract

By defining the problem in terms of time and accuracy, there will be a positive impact on the patient, and this medical imaging problem is still very much open in brain tumor classification. This research has an advanced image segmentation algorithm to present an optimized framework for the classification of brain tumors. The method brings depth-learning segmentation models like U-Net and Fully Convolutional Networks (FCNs) into more classical techniques like thresholding and region growing algorithms to increase the segmentation precision. As a preparation for the dataset of MRI scans that have T1, T2, and FLAIR images, they are preprocessed and enhanced toward better robustness with changing imaging conditions. The segmented tumor regions will be ana-lyzed with feature extraction considering the texture, shape, and intensity features to capture tumor heterogeneity. Convolutional neural networks (CNN) and sup-port vector machines are well-known machine learning algorithms employed in classification and have potential applications that could be defined in a well-structured manner for various types of tumors. Examples of some tumor types include meningiomas, gliomas, and metastases. To optimize the performance of a model, the techniques for making ensembles and the tuning of hyper-parame-ters are utilized. The proposed methods have shown positive results through var-ious evaluation metrics, such as accuracy, precision. It is expected that this will give rise to a substantial improvement in both the accuracy of segmentation and classification of tumor types, with automated brain tumor diagnosis and increased efficiency in clinical decision-making potential.