Performance Analysis of Deep Learning Architectures in Brain Tumor Classification

Abstract

Brain tumors are uncontrolled tissue growths that develop as a result of replication errors during the cell renewal process and are classified as malignant or benign. These tumors can cause brain damage or severe health problems due to increased intracranial pressure. Generally, brain tumors are categorized into two main types: primary and secondary. Primary tumors, often benign, originate directly in the brain, whereas secondary tumors result from the metastasis of cancer cells from other organs to the brain. Early diagnosis of brain tumors is crucial for treatment planning and evaluating outcomes. This study aims to facilitate the early detection and classification of brain tumors using Magnetic Resonance Imaging (MRI). It evaluates the performance of deep learning architectures such as EfficientNet, DenseNet, and AlexNet on three different datasets containing glioma, meningioma, pituitary tumors, and tumor-free brain images. The research compares current deep learning techniques and various performance metrics to identify architectures with the highest performance. The results demonstrate that the developed methods are effective in brain tumor classification. This research aims to assist healthcare professionals in making accurate diagnoses, enabling swift and effective treatment of patients, and significantly contributing to the early diagnosis and treatment planning of brain tumors.