Authors :
Girish K A; Chandana B S; Chandrakala H M; Thejasvi M; Vignesh Kumar G
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/hc82v59d
DOI :
https://doi.org/10.38124/ijisrt/25may1462
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
In today’s medical imaging field, the classification of brain tumors plays a crucial role in determining the
treatment plan, course of therapy, and survival rate. Our approach introduces a new technique that utilizes image-based
deep learning models, specifically pre-trained neural networks, combined with a stacking algorithm for improved
classification of brain tumors. Here, our method begins by processing T1-weighted images from MRI brain scans using
multiple pre-trained CNNs. These neural networks extract visual features from the images, capturing intricate details
crucial for accurate classification. To enhance accuracy, we employ an ensemble technique where the extracted image
features serve as inputs to a single-layer stacking algorithm. This method integrates predictions from multiple base
classifiers to make a final, more robust decision. Through its use of transfer learning, our approach leverages CNNs trained
on extensive image datasets, ensuring that the extracted features are highly relevant for brain tumor classification. The
combination of various base classifiers with a stacking algorithm further enhances classification accuracy. Our evaluation
on two publicly available brain MRI image datasets demonstrates that this method significantly improves lesion detection,
making it a promising step forward in medical imaging and healthcare.
Keywords :
Brain Tumor Classification; MRI; Deep Learning; Convolutional Neural Networks (CNN); Transfer Learning; Stacking Algorithm; Ensemble Learning; Medical Imaging; T1-weighted Images; Feature Extraction.
References :
- M. Sharma, P. Sharma, R. Mittal, and K. Gupta, “BrainTumor Detection Using Machine Learning,” India, 2023.
- S. Raza, N. Gul, H. A. Khattak, and A. Rehan, “Brain Tumor Detection and Classification Using Deep Feature Fusion and Stacking Concepts,” China, Pakistan, UAE, USA, 2024.
- B. B. Vimala and S. Srinivasan, “Detection and Classification of Brain Tumor Using Hybrid Deep Learning Models,” India, Ethiopia, 2023.
- A. Oh, I. Noh, and J. J. Lee, “Machine Learning Approach to Brain Tumor Detection and Classification,” India, 2024.
- S. Solanki, “Brain Tumor Detection and Classification Using Intelligence Techniques,” India, 2023.
- “Understanding Grad-CAM: Visualizing CNN Decisions,” Towards Data Science, https://www.analyticsvidhya.com/blog/2023/12/grad-cam-in-deep-learning/
- “Brain Tumor MRI Dataset”, Kaggle, https://www.kaggle.com/navoneel/brain-mri-images-forbraintumor-detection
In today’s medical imaging field, the classification of brain tumors plays a crucial role in determining the
treatment plan, course of therapy, and survival rate. Our approach introduces a new technique that utilizes image-based
deep learning models, specifically pre-trained neural networks, combined with a stacking algorithm for improved
classification of brain tumors. Here, our method begins by processing T1-weighted images from MRI brain scans using
multiple pre-trained CNNs. These neural networks extract visual features from the images, capturing intricate details
crucial for accurate classification. To enhance accuracy, we employ an ensemble technique where the extracted image
features serve as inputs to a single-layer stacking algorithm. This method integrates predictions from multiple base
classifiers to make a final, more robust decision. Through its use of transfer learning, our approach leverages CNNs trained
on extensive image datasets, ensuring that the extracted features are highly relevant for brain tumor classification. The
combination of various base classifiers with a stacking algorithm further enhances classification accuracy. Our evaluation
on two publicly available brain MRI image datasets demonstrates that this method significantly improves lesion detection,
making it a promising step forward in medical imaging and healthcare.
Keywords :
Brain Tumor Classification; MRI; Deep Learning; Convolutional Neural Networks (CNN); Transfer Learning; Stacking Algorithm; Ensemble Learning; Medical Imaging; T1-weighted Images; Feature Extraction.
