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Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM

Mian Usman Sattar¹, Meznah A Alamro², Alaeddine Mihoub³

¹Department of Computing, University of Derby, Derby, United Kingdom.

Frontiers in Medicine

|June 24, 2026

Summary

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This summary is machine-generated.

This study introduces a novel framework for brain CT scan classification using deep learning, achieving 97.44% accuracy. MobileNetV2 demonstrated superior efficiency and accuracy, making it ideal for clinical use in detecting brain abnormalities.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

Computed Tomography (CT) brain scans are vital for diagnosing neurological conditions like tumors and aneurysms.
Accurate classification of CT brain scans aids in treatment decisions and disease monitoring.
Enhancing the accuracy and interpretability of brain abnormality detection is a key clinical challenge.

Purpose of the Study:

To develop and evaluate a novel framework for brain CT image classification.
To improve the accuracy and interpretability of detecting brain abnormalities using deep learning.
To compare the performance of multiple pre-trained deep learning models for this task.

Main Methods:

Utilized a comprehensive dataset of CT brain scans with Digital Imaging and Communications in Medicine (DICOM) pre-processing.

Keywords:

CT brain scans convolutional neural networks ensemble prediction medical image classification transfer learning

Related Experiment Videos

Employed transfer learning with four state-of-the-art pre-trained models: MobileNetV2, ResNet-50, EfficientNet-B0, and VGG-16.

Incorporated Grad-CAM for model interpretability and ensemble prediction techniques.

Main Results:

MobileNetV2 and the Ensemble model achieved the highest classification accuracy (97.44%) and macro-AUC scores (0.9895-0.9914).
VGG16 showed strong performance with 92.31% accuracy and the highest macro-AUC (0.9962).
MobileNetV2 demonstrated exceptional efficiency with rapid training (89 s) and testing (38.16 s) times.

Conclusions:

MobileNetV2 offers a highly accurate and computationally efficient solution for brain CT image classification.
The proposed framework enhances the detection of brain abnormalities, with potential for clinical deployment.
The study highlights the effectiveness of transfer learning and ensemble methods in medical image analysis.