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NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

Students & Supervisors

Student Authors
Istyak Ahmed
Bachelor of Science in Computer Science & Engineering, FST
Hadiur Rahman Nabil
Bachelor of Science in Computer Science & Engineering, FST
Tazdik Hossain
Bachelor of Science in Computer Science & Engineering, FST
Aritra Das
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Dr. Muhammad Firoz Mridha
Professor, Member [research & Publication Steering Committee], FST

Abstract

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targeting three types: gliomas, meningiomas, and general brain tumors, using Magnetic resonance imaging (MRI) data. The framework is estimated on the Bangladesh Brain Cancer MRI Dataset, which consists of 6,056 MRI images divided into three balanced classes. Unlike traditional machine learning techniques that rely on handcrafted features, NeuroNet’s architecture leverages CNN layers with a spatial attention mechanism, enabling the model to focus on critical tumor regions. This approach reduces manual intervention and enhances feature representation. The proposed model performed a high classification accuracy of 99.04%, with a weighted F1-score of 99.02%, outperforming conventional models. These results indicate NeuroNet’s potential to assist medical professionals by providing an accurate and reliable diagnostic tool, thus improving brain tumor treatment outcomes. This research underscores the promise of attention mechanisms within deep learning frameworks for advancing cancer diagnosis and medical imaging.

Keywords

Image Classification CNN Attention Mechanism Brain Tumor Medical Imaging.

Publication Details

  • DOI: DOI: 10.1109/DASA63652.2024.10836274
  • Type of Publication: Conference 
  • Conference Name: 2024 International Conference on Decision Aid Sciences and Applications (DASA)
  • Date of Conference: 11/12/2024 - 11/12/2024
  • Venue: Applied Science University in Bahrain, Kingdom of Bahrain
  • Organizer: Applied Science University in Bahrain, Kingdom of Bahrain