Machine and Deep Learning Approaches For Brain Tumor Identification: Technologies, Applications, and Future Directions

Brain tumor classification and detection utilizing machine learning and deep learning are increasingly becoming the focus of medical image analysis research. Thanks to the advancement of modern imaging technology and machine learning algorithms, it is now possible to accurately and efficiently classify and diagnose brain tumors using a range of methodologies. Machine learning and deep learning algorithms are currently utilized to assess medical images and categorize tumors based on traits such as shape, size, and texture. Convolutional neural networks (CNNs), a popular deep learning technique, are utilized to identify brain tumors in medical pictures precisely. These models were created using large datasets of brain MRI scans and are incredibly accurate in spotting the presence of cancer. These models have been tested and trained using a variety of freely available datasets, such as the Brain Tumour Segmentation (BraTS) dataset and the LGG/GBM dataset. Researchers have developed several methods for preprocessing the data, increasing the dataset, and fine-tuning the models to improve the models' accuracy further. Overall, categorizing and identifying brain tumors using machine learning and deep learning is a rapidly growing field promising to enhance patient outcomes. By accurately and successfully identifying brain tumors, these techniques can lead to earlier detection and more tailored treatments, ultimately improving patient outcomes. The categorization and detection of brain tumors have been enhanced thanks to machine learning and deep learning on various datasets.