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Reply to Pastore, E.P. Comment on "Rastogi et al. Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks. <i>Life</i> 2025, <i>15</i>, 327".

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Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within

Deependra Rastogi1, Prashant Johri2, Massimo Donelli3,4

  • 1School of Computer Science and Engineering, IILM University, Greater Noida 201306, India.

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|March 27, 2025
PubMed
Summary

This study enhances brain tumor classification using artificial intelligence (AI) and deep transfer learning. The Xception model achieved 96.11% accuracy in detecting brain tumors from MRI scans.

Keywords:
InceptionResNetV2MobileNetV2VGG19Xceptionaugmentationbrain tumordeep learningfine-tuneimage processingtransfer learning

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Machine Learning

Background:

  • Brain tumor diagnosis is complex due to brain anatomy and tumor heterogeneity.
  • Magnetic Resonance Imaging (MRI) is crucial but accurate tumor detection remains challenging.
  • Deep learning automates feature extraction from high-dimensional MRI data for precise diagnoses.

Purpose of the Study:

  • To enhance brain tumor classification using fine-tuned deep transfer learning architectures.
  • To evaluate the performance of various transfer learning models for improved tumor detection.
  • To leverage AI for more accurate and efficient brain tumor diagnosis.

Main Methods:

  • Employed deep transfer learning models: InceptionResNetV2, VGG19, Xception, and MobileNetV2.
  • Utilized a Kaggle dataset of brain MRI images (tumor and non-tumor).
  • Applied image augmentation to address class imbalance and pre-trained models for fine-tuning.

Main Results:

  • The Xception model demonstrated superior performance, achieving 96.11% accuracy.
  • Fine-tuned transfer learning models significantly improved tumor versus non-tumor classification.
  • The study confirmed the effectiveness of AI in analyzing complex MRI data.

Conclusions:

  • Fine-tuned deep transfer learning architectures, especially Xception, substantially enhance brain tumor diagnosis accuracy and efficiency.
  • Advanced AI models show great potential in supporting clinical decisions for better patient outcomes.
  • This research highlights the capability of AI in high-precision brain tumor detection from MRI scans.