A Model for Brain Tumor Detection Using a Modified Convolution Layer ResNet-50
A Model for Brain Tumor Detection Using a Modified Convolution Layer ResNet-50
Rakesh Kumar,Abhishek Kumar Mishra,4 Authors,Pranati Waghodekar
TLDR
The suggested model’s key tool is the Convolutional Neural Network model ResNet-50, which achieves an impressive accuracy rate of 81.6 percent, and the model’s performance exceeds expectations.
Abstract
Tumors are the second most prevalent type of cancer, posing a serious concern to many individuals due to their unregulated tissue development. Efficient approaches for identifying tumors, particularly brain cancer, quickly, automatically, precisely, and correctly, are crucial in the medical industry. When cancer is appropriately recognized, early identification plays a critical role in effective treatment, ensuring patient safety. Tumors form as a result of uncontrolled cell development, causing the slow degeneration of brain tissue as they consume resources meant for healthy cells and tissues. While Magnetic Resonance Imaging (MRI) is used to examine images to establish tumor location and size, the procedure is inefficient and time-consuming. The suggested model’s key tool is the Convolutional Neural Network (CNN) model ResNet-50, which achieves an impressive accuracy rate of 81.6 percent. As expected, the model’s performance exceeds expectations.