Evaluation of different deep learning models in the detection of brain tumor in MRI images

Evaluation of Different Deep Learning Models in the Detection of Brain Tumors in MRI Images Student's Name Institution Course # and Name Professor's Name Submission Date Introduction Background Information Brain tumors are considered one of the most severe health conditions because they can severely compromise one's health and general life quality. Infiltration of normal brain tissue with brain tumors leads to increased intracranial pressure and severe neurologic consequences. This is a massive threat to someone's well-being and physical safety. Brain cancers are the most common and deadliest primary tumors in person. Moreover, children who survive cancer to adulthood may have lasting effects from medical treatments such as radiation, chemotherapy, and surgery on their growing brains (Aldape et al., 2019). The biological characteristics of these tumors that often impede progress make them challenging to treat. CNS disorders like stroke, infection, brain tumors, or migraines pose significant challenges in terms of diagnosing, evaluating, and developing effective remedies. Brain tumors are less frequent than other cancers; therefore, pharmaceutical organizations fund and pay less attention to them, dividing and shrinking the scientific community. Brain and CNS tumors vary in their occurrence, ranging from extremely rare to often diagnosed. They also differ in their potential for injury, ranging from non-malignant to cancerous (Franceschi et al., 2020). Primary brain tumors and secondary or metastatic brain tumors vary in terms of origin. The latter spreads to the brain from other body parts, while the Primary starts in the brain. Primary brain tumors are neoplasms that develop within the brain itself. Timely identification can significantly enhance the likelihood of a favorable treatment outcome, reducing mortality rates and enhancing quality of life. On the other hand, if the condition is not discovered right once, it often advances to more severe stages, which makes therapy harder and less effective. The Significance of Using MRI Imaging for Brain Tumor Detection Brain tumors are often identified using conventional approaches that rely on the manual interpretation of medical imaging, such as CT and MRI scans. These methods are effective yet time-consuming and may have consistency and accuracy issues. Manual MRI-based brain tumor detection is complicated and error-prone. Non-invasive magnetic resonance imaging (MRI) techniques produce exact and comprehensive images of various interior body structures, encompassing blood vessels, muscles, bones, and organs. The MRI scanner employs radio waves and a robust magnet to generate human body images (Smith, 2024). MRIs do not produce ionizing radiation, unlike X-rays. Doctors require these photos to diagnose and treat patients. MRI is preferred over CT scans for brain tumor detection. MRI can distinguish normal and cancerous brain tissues better due to its superior contrast resolution. MRI provides detailed brain tumor size, position, and scope data, which is essential for accurate diagnosis and therapy planning. Introduction to Deep Learning Models and their Application in Medical Image Analysis Due to advances in imaging tools and the requirement for fast, accurate, and automated image interpretation, deep learning is taking over medical image processing. Deep learning is the most advanced machine learning method in many applications. Deep learning uses multi-layer neural networks for representation learning. This system converts incoming data into numerous abstractions to learn data representations automatically. Deep learning has revolutionized medical image processing, offering new ways to detect and analyze diseases like brain tumors (Bati & Ser, 2023). Convolutional neural networks (CNNs) can identify and classify brain tumors by learning complex MRI patterns. Supervised learning trains CNN on sizeable medical image collections. CNNs have transformed computer vision and are used in speech recognition and NLP (Krichen, 2023). CNNs mimic the human eye to quickly process and evaluate visual data like photos and videos. Medical image denoising, segmentation, and classification rely on it to ensure precise analysis. DL has emerged as a powerful method for image analysis, with promising results in lowering noise in various medical imaging. The information generated is comprehensive and precise to identify illnesses using MRI, PET, Ultrasound, and X-ray, even in the presence of noise (Muksimova et al., 2023). These neural networks strive to duplicate their input data, and when trained on noisy medical images, they proficiently learn to produce their clearer equivalents. Rayed et al. (2024) have shown considerable advancements in precisely identifying various skin structures and lesions using CNNs, transfer learning, and deep residual networks. In addition to detecting tumors, DL can improve healthcare, especially in areas that use medical imaging for diagnosis, prediction, and treatment (Huang et al., 2020). Informative and accurate data describing the present symptoms and previous medical background results in a more valuable report for the physicians and an optimal outcome for the patient. Objective and Scope of the Study The purpose of this research is to examine the performance of different DL models on MRI images to detect brain tumors. The effectiveness of four widely used CNN architectures, YOLOv9, YOLOv8, Faster R-CNN, and ResNet18, will be assessed in this study. The selection of these models is based on their proven capability to perform various image classification tasks, including medical imaging. The main objective of this study is to examine the patterns, difficulties, and areas of limited understanding identified in previous research on the use of DL techniques for brain tumor identification. This research seeks to improve brain tumor CAD systems by filling these shortcomings. The evaluation of these models will improve early identification, patient outcomes, and clinical treatment planning. Literature Review Detection of Brain Tumors Using MRI Images and DL Models The subject of MIA has attracted substantial attention and research due to its extensive applications in the healthcare sector when evaluating and diagnosing patients. In their study, Abdusalomov et al. (2023) discovered that machine learning can categorize brain scans and examine the brain's structure. Deep learning is extensively employed to automate the identification and segmenting of brain tumors. They used several brain tumor images to identify brain cancers in MRI scans. Through transfer learning, the researchers increased a "state-of-the-art YOLOv7" model's ability to detect pituitary brain tumors, meningiomas, and gliomas. Abdusalomov et al. (2023) found that deep learning has yielded several valuable applications in pattern categorization. ZainEldin and colleagues explored a more accurate brain tumor diagnosis algorithm in 2023. ZainEldin et al. (2023) discovered that the BCM-CNN classifier performed best. This was due to CNN hyperparam...