Machine Learning and Deep Learning Approaches for Brain Tumor Diagnosis

Abstract

One of the well-known and intricate issues in clinical neuro-imaging lies in interpreting the results of a brain tumor diagnosis. The manual process of diagnosing a patient has benefited greatly from the evolution of machine learning (ML) technologies as they can now help with faster and more accurate results, boosting the chances of more effective treatment of patients. The main focus of this research is to analyze and compare the performance of deep learning models such as Convolutional Neural Networks (CNN) and other classical ML methods, like Support Vector Machines (SVM), Random Forests (RF), and Logistic Regression (LR), in identifying and synthesizing the types of brain tumors from MRI scans. The BRASTS202X dataset selected for the research includes sets of MRI images with different types of annotated tumors. Also, preprocessing steps such as skull stripping, intensity normalization, and motion correction were applied to the dataset. The conventional ML models were provided with raw images from which they had to learn features directly. The handcrafted features included the shape and texture of the tumor. Evaluation of the findings showed that most of the metrics: accuracy, sensitivity, specificity, F1, and AUC were higher when using deep learning models. In addition, the implementation of data augmentation strategies improved the performance of deep learning models, particularly in low data environments. Our findings support the hypothesis that the potential for brain tumor diagnosis using deep learning approaches is far greater than traditional ML techniques, provided it is combined with appropriate training and data augmentation. However, these novel techniques require cautious accounts of the data volume and computational resources for effective clinical use.

Keywords: Brain Tumor Diagnosis, Machine Learning, Deep Learning, Convolutional Neural Networks, MRI, Classification, Segmentation, Data Augmentation, Support Vector Machine, Random Forest, Model Evaluation