Toward the Detection of Brain Tumor Using MRI Images: A Deep Learning-Based Approach
Book Chapter
Soni, J, Upadhyay, H. (2026). Toward the Detection of Brain Tumor Using MRI Images: A Deep Learning-Based Approach
. Part F1428 147-158. 10.1007/978-3-031-89700-9_9
Soni, J, Upadhyay, H. (2026). Toward the Detection of Brain Tumor Using MRI Images: A Deep Learning-Based Approach
. Part F1428 147-158. 10.1007/978-3-031-89700-9_9
In their peak grade, the brain tumors are very aggressive and lead to a small life expectancy. Misdiagnosis of brain tumor types will lessen the survival chance of patients and preclude the real response to medical involvement. Consequently, patients’ quality of life can be improved with proper planning of treatment. Commonly, to assess the tumor in a brain, breast, lung, liver, etc., numerous imaging systems such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound image are considered. MRI scans of the patient’s brain differentiate between benign and malignant brain tumors. Medical experts assess MRI scans manually for precise results. This approach is time-intensive and prone to human errors due to numerous distinct variations of brain tumors and a large amount of data generated through MRI scans. Hence, it is essential to have a trusted classification system to prevent the death of the patient. Varied structural and spatial adjacent region of brain tumor makes the development of automatic brain tumor classification a very challenging task. Recently, deep learning and big data are promising results in solving various problems. This chapter addresses the problem of brain tumor detection using a deep learning-based approach. We implement brain tumor detection by proposing a learning-based framework on the open-source brain MRI images for brain tumor detection dataset. This dataset is available on Kaggle. The proposed framework uses CNN to detect brain tumors using advanced deep learning frameworks like TensorFlow. This approach can be extended to classify the MRI images of other neurological disorder areas.
