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Updated: Jan 15, 2026

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Advanced deep learning-based brain tumor classification using a novel customized CNN and optimized residual network.

Mehwish Rasheed1, Sajid Iqbal2, Arfan Jaffar1

  • 1Faculty of Computer Science and Information Technology, The Superior University, Lahore, Pakistan.

Plos One
|October 10, 2025
PubMed
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This study introduces two deep learning models for brain tumor classification. The optimized ResNet101 model demonstrated superior performance in accurately categorizing brain tumors from MRI images.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors, characterized by uncontrolled cell growth, pose significant health risks if untreated.
  • Accurate detection and classification are crucial for understanding tumor mechanisms and guiding effective treatment strategies.
  • Challenges in brain tumor detection include variations in size, structure, and location, necessitating advanced analytical methods.

Purpose of the Study:

  • To develop and evaluate deep learning (DL) models for classifying brain tumor images.
  • To compare the performance of a novel customized Convolutional Neural Network (CNN) against an optimized ResNet101 model.
  • To classify brain tumor images into four categories: gliomas, pituitary tumors, meningiomas, and no tumor.

Main Methods:

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  • Utilized a dataset of 3,264 MRI images from Kaggle.
  • Implemented and trained two DL models: a novel customized CNN and an optimized ResNet101.
  • Employed five-fold cross-validation for model training and validation, followed by evaluation on a separate test set.

    • Main Results:

    • The optimized ResNet101 model achieved higher performance than the customized CNN.
    • Average training accuracies were 99.03% (CNN) and 99.87% (ResNet101) across cross-validation folds.
    • Testing accuracies reached 97.72% (CNN) and 98.73% (ResNet101), with ResNet101 demonstrating superior results.

    Conclusions:

    • Deep learning models show significant potential in supporting clinical decision-making for brain tumor classification.
    • The optimized ResNet101 model is a promising tool for accurate and efficient brain tumor diagnosis.
    • These advancements in AI-driven analysis can contribute to improved patient survival rates and health outcomes.