Benign vs. Malignant Brain Tumors: An In-Depth Review Using Deep Learning Techniques

The brain's complex web of connections is essential for enabling functions like movement, sensation, and memory. The intricate and delicate nature of the brain underscores its significance as a critical organ in the human body. Uncontrolled cell growths, or tumors, can significantly affect the brain's operation, similar to their effects on other body parts. Without timely detection and proper treatment, these tumors pose a serious health risk, potentially leading to life-threatening conditions. Brain tumors have two main classes: Malignant Tumors and Benign Tumors. The tumors that are cancerous, and have the capability to spread within the brain or to other body regions fall in the category of Malignant Tumors, whereas the tumors that are non-cancerous and do not spread from their original location fall under the category of Benign Tumors. Identifying these tumors poses a significant challenge, especially within the brain's complex structure, complicating the diagnosis process for medical professionals. Although MRI scans and CT scans are positively effective in determining the type, size, and location of tumors, there remains an essential need for more accurate detection methods. Advanced Deep Learning algorithms and Machine Learning algorithms are two examples of the increasingly complex algorithms that have been developed in response to the growing requirement for precise and accurate tumor identification. These algorithms are essential for improving the precision and accuracy of detection. Between 2018 and 2023, researchers have developed various techniques leveraging these algorithms for brain tumor identification, offering improved precision, effectiveness, and efficiency over traditional methods. This review analyzed 12 such research studies, using their findings to establish the groundwork for discussing methodologies, limitations, and results. The research specifically focuses on employing cutting-edge deep-learning algorithms to analyze Tl-weighted MRI data for brain tumor detection. This study aims to offer a detailed overview of the latest advancements in brain tumor identification techniques. It aims to provide insightful observations and recommendations for future researchers to improve the precision and make the rate at which tumor diagnosis methods work faster, potentially leading to a quicker and more accurate detection of brain tumors, thereby saving lives.