Brain Tumor Detection Using Deep Learning
1 Prakash Bethapudi, 2 Induri Madhava Reddy, 3 Jagadam Sanjuja 4 Gollapalli Geetha Siva Ganga Santosh, 5 Gollapudi Sai Mounika 1 Professor, 2˒3˒4˒5 B.Tech IV Year,
Andhra University, Visakhapatnam, Andhra Pradesh, India Corresponding email : prakash.vza@gmail.com
imr@outlook.in sanjujajagadam@gmail.com iamsanthoshgollapalli@gmail.com ssaimouni2002@gmail.com
Abstract The human brain is one of the most complex organs in the body, comprising billions of cells. A brain tumor arises when there is uncontrolled cell division, resulting in an abnormal mass of tissue either inside or around the brain. This group of abnormal cells can interfere with the normal functioning of the brain and damage healthy tissue. Brain tumors are generally classified into two categories: Benign (non-cancerous) and Malignant (cancerous). Detecting and classifying brain tumors is one of the most challenging and time-consuming tasks in the field of medical image analysis. With the rapid advancement of technology, computer vision is playing an increasingly vital role in healthcare, especially in medical diagnostics. Among various imaging techniques such as CT scans, X-rays, and MRIs, Magnetic Resonance Imaging (MRI) is considered the most reliable and safe for brain imaging. Traditionally, doctors manually examine MRI scans to locate and measure brain tumors, which is often time-intensive and prone to human error. In this project, we propose a deep learning-based approach using Convolutional Neural Networks (CNNs) to accurately detect and classify brain tumors. We trained, tested, and validated our CNN model on a brain tumor MRI dataset. The architecture includes multiple convolutional layers to facilitate effective feature extraction and improve prediction accuracy. CNNs are among the most powerful tools in deep learning and are widely used in research and practical applications.
Keywords Brain Tumor, MRI (Magnetic Resonance Imaging), Deep Learning, Convolutional Neural Networks (CNN) ,Medical Image Analysis, Tumor Detection, Tumor Classification, Computer Vision, Benign and Malignant Tumors, Healthcare Diagnostics
