Innovative Machine Learning Techniques for Brain Tumor Classification in MRI Using Hybrid Approach

Medical imaging is critical to the detection, classification, and classification of brain tumors in magnetic resonance imaging (MRI). This work highlighted the shortcomings of manual classification methods, which led to further investigation of the methods. The methods include preprocessing methods such as contrast limited adaptive histogram equalization (CLAHE) and anisotropic diffusion to enhance image detail Phase I The research focused on the application of machine learning techniques, i.e. convolutional neural networks (CNNs) and clustering algorithms to classify brain tumors in MRI images Performance issues of existing classification methods were also addressed, as their low accuracy is limited by use images Based on the findings of phase 1, phase 2 of the study proposed a unique brain tumor classification method combining CNN and machine support vector (SVM). It also included feature extraction using CNNs to improve accuracy. The CNN-SVM model outperformed the conventional deep learning methods in terms of classification results, which showed promise for tumor segmentation and classification accuracy Finally, the comparison results suggest the importance of medical imaging techniques, especially in the diagnosis of brain tumors with MRI. The proposed CNN-SVM algorithm shows promise to overcome the limitations of existing manual classification methods and deep learning methods, resulting in greater accuracy and reliability in clinical cases