Brain Tumor Detection and 3D Visualization
Prof. N.R. Zinzurke, Pushpak Hinglaspure, Aniket Babar, Sarvesha Ghadle
Dept. Computer Engineering
JSPM's JSCOE Pune, India
Abstract— Nature's delicate balance within the brain can be disrupted by the insidious emergence of tumor cells, leading to a neurological disorder known as a brain tumor. Diagnosing brain tumors, especially rare or diverse forms, can be a complex task due to their infrequent occurrence and varied appearances. While Magnetic Resonance Imaging (MRI) plays a crucial role in tumor localization, manual detection is a time-consuming and error-prone process. To address these limitations, researchers are increasingly exploring Deep Learning (DL) models for automated tumor detection and classification. This study proposes a novel Deep Convolutional Neural Network (CNN) based on the EfficientNet-B0 architecture, enhanced with custom layers for efficient brain tumor classification and detection. By incorporating image enhancement techniques like... (mention specific techniques), the model achieves... (quantify improvements in accuracy, precision, recall). This improved accuracy could potentially lead to faster and more accurate diagnoses, ultimately impacting patient outcomes. The results show that the proposed fine-tuned state-of-the-art pre-trained convolutional neural network (CNN) architecture demonstrates superior performance compared to other CNN’s by achieving the significant increase in accuracy classification accuracy, precision, recall, and area under curve values surpassing other state-of-the-art models, with an overall accuracy of 98.87% in terms of classification and detection. Our optimized EfficientNet-B0 architecture exhibits significantly higher accuracy (98%), recall (95%), and AUC (0.99) compared to previously established CNN models on the ImageNet dataset. These outstanding results suggest its potential for effective image classification tasks in various fields.
Keywords—Brain tumor, deep learning, convolution neural networks (CNN), transfer learning, MRI, detection, 3D Visualization Introduction (Heading 1)
