Brain Tumor Detection and Classification using Deep Learning

Abstract

Abstract: This paper explores the application of convolutional neural networks (CNNs) in diagnosing brain tumors, focusing on children and the elderly who are most vulnerable to this type of cancer. Brain tumors pose significant health risks due to uncontrolled cell growth within the skull, making accurate classification challenging. The study utilizes CNNs, a popular machine learning approach, to classify 3260 brain magnetic resonance images into glioma, meningioma, pituitary, and no tumor categories. Three algorithms, CNN, VGG, and Densenet, are employed for classification. Early detection is vital for effective treatment, and computer-aided diagnostics show promise in assisting clinicians. This work provides an overview of traditional computer-aided tumor diagnosis and highlights the advantages of using CNNs. It discusses image segmentation, classification techniques based on CNN, Densenet, and VGG16, and serves as a guide for future tumor detection research. Data augmentation and min-max normalization enhance tumor cell contrast. The dense CNN model accurately classifies a small image database, and Densenet demonstrates remarkable performance. Experimental results show CNN achieving 45% accuracy on testing data, VGG achieving 51%, and Densenet achieving an impressive 86.49% during training, with high precision and a good F1 score. Overall, this paper emphasizes the potential of CNNs for brain tumor diagnosis and contributes to the development of computeraided diagnostic systems. It advances the field of medical image analysis and paves the way for further research in tumor detection.