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An Explainable Multi-Scale Deep Learning Framework for Multi-Class Brain MRI Classification.

Hamoud H Alshammari1, Mahmood A Mahmood1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|June 26, 2026
PubMed
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This study introduces MCND-ComputeNet++, a deep learning framework that accurately classifies brain MRI scans into eight categories. The model demonstrates strong performance and reliable confidence estimation for neurological disorder assessment.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Brain magnetic resonance imaging (MRI) is crucial for diagnosing neurological disorders.
  • Automatic multi-class MRI classification faces challenges like visual similarity, class imbalance, and unreliable confidence estimation.

Purpose of the Study:

  • To develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification.
  • To address the challenges in multi-class MRI classification using a novel deep learning approach.

Main Methods:

  • Developed MCND-ComputeNet++, a framework utilizing a pretrained EfficientNetV2-S backbone for hierarchical feature extraction.
  • Employed adaptive multi-scale fusion, convolutional refinement, and spatial attention pooling.
  • Implemented a training strategy including class-balanced focal loss, label smoothing, MixUp/CutMix, EMA weight smoothing, cosine learning-rate scheduling, temperature scaling, and test-time augmentation.
Keywords:
EfficientNetV2-SGrad-CAM explainabilitybrain MRI classificationgated feature fusionmulti-class neurological diagnosis

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Main Results:

  • MCND-ComputeNet++ achieved high performance with mean accuracy (0.9738), macro-F1 (0.9771), macro-AUC (0.9993), and macro-average precision (0.9971).
  • The model outperformed several baseline models, including ResNet50, DenseNet121, EfficientNetB0, Swin-Tiny, and ConvNeXt-Tiny.
  • Demonstrated improved discrimination and confidence reliability compared to ConvNeXt-Tiny and a standard EfficientNetV2-S classifier.

Conclusions:

  • MCND-ComputeNet++ shows promise as an image-level brain MRI classification framework for eight categories.
  • The integrated architecture enhances feature extraction, fusion, refinement, and calibrated inference.
  • Further validation at the patient-level with diverse datasets and multimodal information is necessary for clinical applicability.