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Deep transfer learning based feature fusion model with Bonobo optimization algorithm for enhanced brain tumor

Pradeep Gurunathan1, Preethi Saroj Srinivasan2, Ravimaran S3

  • 1School of Computing, Sastra Deemed to be University, Thanjavur, Tamil Nadu, India.

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|October 1, 2025
PubMed
Summary

This study introduces an AI model for enhanced brain tumour segmentation and classification using biomedical imaging. The proposed method achieves 99.16% accuracy, improving early diagnosis and patient outcomes.

Keywords:
Biomedical imagingBonobo optimization algorithmBrain tumor segmentationFeature model fusionImage Pre-processing

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Oncology and Diagnostic Technologies

Background:

  • Brain tumours (BTs) are aggressive, necessitating early detection and treatment for improved patient prognosis.
  • Current manual segmentation of brain tumours from biomedical images is time-consuming and subjective.
  • Artificial intelligence (AI) offers a promising avenue for automating and enhancing medical image analysis.

Purpose of the Study:

  • To propose an Enhanced Brain Tumour Segmentation through Biomedical Imaging and Feature Model Fusion with Bonobo Optimiser (EBTS-BIFMFBO) model.
  • To improve the accuracy and efficiency of brain tumour segmentation and classification using advanced AI techniques.
  • To leverage feature fusion and optimization methods for robust brain tumour diagnosis.

Main Methods:

  • Pre-processing involved bilateral filter (BF) for noise reduction and CLAHE for contrast enhancement.
  • DeepLabV3+ model was utilized for accurate tumour region segmentation.
  • Feature extraction employed a fusion of InceptionResNetV2, MobileNet, and DenseNet201 models.
  • Classification was performed using a convolutional sparse autoencoder (CSAE) optimized by the Bonobo Optimizer (BO).

Main Results:

  • The EBTS-BIFMFBO model demonstrated superior performance in brain tumour segmentation and classification.
  • The proposed approach achieved a high accuracy of 99.16% on the Figshare BT dataset.
  • The feature fusion and BO optimization significantly contributed to the model's effectiveness.

Conclusions:

  • The EBTS-BIFMFBO model represents a significant advancement in AI-driven brain tumour diagnosis.
  • The study highlights the potential of integrated AI approaches for accurate and efficient medical image analysis.
  • This enhanced segmentation and classification method can aid clinicians in timely therapeutic planning and improve patient survival rates.