Abstract:
Brain tumors can be a life-threatening condition, and early detection is crucial for effective treatment. Magnetic resonance
imaging (MRI) is a valuable appliance for identifying the tumor's location, but manual detection is a time-engrossing and flaws-prone
process. To overcome these challenges, computer-assisted approaches have been developed, and deep learning (DL) archetypes are
now being pre-owned in medical imaging to discover brain tumors maneuver MRI carbon copies. In this, we propose a deep
convolutional neural network (CNN) Xception net model for the efficient classification and detection of brain tumor images. The
Xception net is a powerful CNN model that has shown promising results in various systems perceiving exercise, in conjunction with
medical illustration scrutiny. We fine-tuned the Xception net model using a dataset of Magnetic Resonance Imaging (MRI) images of
the brain, which were pre-processed and labeled by medical experts. To reckon the performance of our prototype, we counselled dossier
using a variety of interpretation criterion, including accuracy, precision, recall, and F1 score. Our customs view that the urged model
achieved high accuracy in classifying brain tumor images. The archetype’s strength to accurately and efficiently classify and detect
brain tumors using MRI images can significantly improve patient outcomes by enabling early detection and treatment. Overall, our
study demonstrates the persuasiveness of using the Xception net flawless for brain tumor ferreting out and alloting using MRI images.
The proposed model has the potential to revolutionize the department of salutary exemplify and improve patient outcomes for brain
tumor treatment.
