A Machine Learning – Based Classification And Prediction Technique For Brain Tumor

The project's main goal is to solve the time-consuming and expensive problems that come with using MRI scans to classify brain cancers. Brain cancers need a lot of effort and may not always be economical to diagnose manually, noting the requirement for an automated remedy. The main objective of the project is to use artificial intelligence (AI) techniques to create an automatic diagnosis system. The goal of the system is to offer a rapid and precise method for differentiating between patients with glioma brain tumors and pituitary tumors. High-quality photos from medical devices may help in the diagnosis and early detection of various diseases. But the diagnosing procedure could take a while, and gathering and storing pictures like this can be expensive. Synthetic AI-driven automated diagnostics could play a significant role in addressing the issues of time and expense. Pre-trained deep-learning models might offer an efficient method for categorizing medical images. In this study, we suggest two pretrained models like ResNext101_32×8d and VGG19 to categorize two kinds of brain tumors like pituitary and glioma. The suggested models are utilized on a dataset that includes 1,800 MRI images consisting of two categories of diagnoses. Single-image super-resolution (SISR) A method is utilized on the MRI images to categorize and improve their fundamental characteristics, allowing the suggested models to improve specific features of the MRI images and aid the training process of the prototypes. We developed these models utilizing the PyTorch and TensorFlow frameworks with adjusting hyper-parameters and enhancing data. The evaluation of the models is examined. utilizing different metrics, and the findings show that the elevated testing accuracies and minimal loss fees for both models. The suggested models, when utilized on MRI images, aim to deliver a swift and precise method for differentiating between patients with pituitary and glioma tumors. These models may assist physicians and radiologists in the evaluation of patients with brain tumors.