Enhancing Brain Anomaly Pre-Diagnosis with Traditional Artificial Intelligence and Popular Deep Learning Models: A Comparative Analysis

This study primarily aims at the pre-diagnosis and prediction of specific brain tumors by applying traditional and popular segmentation methods with deep learning models and also investigates the comparative performance between Artificial Intelligence (AI) and Deep Learning (DL) methods and models.The diagnostic methods currently used are generally subjective, time-consuming, and require highly specialized knowledge in detail.To determine and overcome these limitations, we propose the well-developed implementation of two deep learning segmentation methods capable of accurately and efficiently analyzing brain tumor based Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT) radiological imaging data.These models were the Support Vector Machine (SVM) for the traditional AI model and ResNet50 and InceptionV3 for the popular DL model architectures, and these were used for diagnosing specific important brain conditions, including ischemic stroke, low-grade glioma (LGG), and normal (tumor-free) cases.In addition, in the medical field, ischemic stroke and LGG images could not be well determined, and misdiagnosing could occur.Because of these reasons, by using these deep learning models, the problems and limitations were overcome.The initial phase involved the meticulous collection and pre-processing of a large open-source/public dataset of MRI and CT images, carefully distinguishing those from ischemic stroke and LGG patients and healthy individuals.The models underwent rigorous training using the pre-processed image dataset and were assessed using various accuracy metrics.While traditional methods utilizing Support Vector Machines (SVM) achieved an accuracy of 77%, deep learning architectures exhibited significant advancements, with ResNet50 and InceptionV3 achieving accuracies of approximately 97%.The InceptionV3 model's lightweight architecture, integrated with effective data augmentation and transfer learning strategies, demonstrated exceptional diagnostic efficiency and accuracy.These results underscore the immense potential of deep learning in revolutionizing brain tumor/lesion diagnosis.