VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).

Journal: Microscopy research and technique
PMID:

Abstract

The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights. VGG16, known for its depth and high performance, is utilized for this purpose. The study demonstrates the model's potential for precise and effective diagnosis by examining how well it can differentiate between areas of normal brain tissue and cancerous regions, leveraging both MRI and microscopy data. We describe in full the pre-processing actions taken to improve the quality of input data and maximize model efficiency. A carefully selected dataset, incorporating diverse tumor sizes and types from both microscopy and MRI sources, is used during the training phase to ensure representativeness. The proposed modified VGG19 model achieved 98.81% validation accuracy. Despite good accuracy, interpretation of the result still questionable. The proposed methodology integrates explainable AI (XAI) for brain tumor detection to interpret system decisions. The proposed study uses a gradient explainer to interpret classification results. Comparative statistical analysis highlights the effectiveness of the proposed explainer model over other XAI techniques.

Authors

  • Deep Kothadiya
    Artificial Intelligence and Data Analytics Lab (AIDA), CCIS, Prince Sultan University, Riyadh 11586, Saudi Arabia.
  • Amjad Rehman
    College of Computer and Information Systems, Al Yamamah University, Riyadh, 11512, Saudi Arabia.
  • Bayan AlGhofaily
    Artificial Intelligence & Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
  • Chintan Bhatt
    U & P U. Patel Department of Computer Engineering, CSPIT, Charotar University of Science and Technology (CHARUSAT), Changa, India.
  • Noor Ayesha
    School of Clinical Medicine, Zhengzhou University, Zhengzhou, China.
  • Tanzila Saba
    College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.