Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks
Brain tumor diagnosis is a complex task due to the intricate anatomy of the brain and the heterogeneity of tumors. While magnetic resonance imaging (MRI) is commonly used for brain imaging, accurately detecting brain tumors remains challenging. This study aims to enhance brain tumor classification v...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Life |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1729/15/3/327 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850279880143405056 |
|---|---|
| author | Deependra Rastogi Prashant Johri Massimo Donelli Lalit Kumar Shantanu Bindewari Abhinav Raghav Sunil Kumar Khatri |
| author_facet | Deependra Rastogi Prashant Johri Massimo Donelli Lalit Kumar Shantanu Bindewari Abhinav Raghav Sunil Kumar Khatri |
| author_sort | Deependra Rastogi |
| collection | DOAJ |
| description | Brain tumor diagnosis is a complex task due to the intricate anatomy of the brain and the heterogeneity of tumors. While magnetic resonance imaging (MRI) is commonly used for brain imaging, accurately detecting brain tumors remains challenging. This study aims to enhance brain tumor classification via deep transfer learning architectures using fine-tuned transfer learning, an advanced approach within artificial intelligence. Deep learning methods facilitate the analysis of high-dimensional MRI data, automating the feature extraction process crucial for precise diagnoses. In this research, several transfer learning models, including InceptionResNetV2, VGG19, Xception, and MobileNetV2, were employed to improve the accuracy of tumor detection. The dataset, sourced from Kaggle, contains tumor and non-tumor images. To mitigate class imbalance, image augmentation techniques were applied. The models were pre-trained on extensive datasets and fine-tuned to recognize specific features in MRI brain images, allowing for improved classification of tumor versus non-tumor images. The experimental results show that the Xception model outperformed other architectures, achieving an accuracy of 96.11%. This result underscores its capability in high-precision brain tumor detection. The study concludes that fine-tuned deep transfer learning architectures, particularly Xception, significantly improve the accuracy and efficiency of brain tumor diagnosis. These findings demonstrate the potential of using advanced AI models to support clinical decision making, leading to more reliable diagnoses and improved patient outcomes. |
| format | Article |
| id | doaj-art-5bb93ae76e2c449a985dedf5f97a812a |
| institution | OA Journals |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-5bb93ae76e2c449a985dedf5f97a812a2025-08-20T01:48:57ZengMDPI AGLife2075-17292025-02-0115332710.3390/life15030327Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning FrameworksDeependra Rastogi0Prashant Johri1Massimo Donelli2Lalit Kumar3Shantanu Bindewari4Abhinav Raghav5Sunil Kumar Khatri6School of Computer Science and Engineering, IILM University, Greater Noida 201306, IndiaSchool of Computing Science and Engineering, Galgotias University, Greater Noida 203201, IndiaDepartment of Civil, Environmental, Mechanical Engineering University of Trento, 38100 Trento, ItalySchool of Computer Science and Engineering, IILM University, Greater Noida 201306, IndiaSchool of Computer Science and Engineering, IILM University, Greater Noida 201306, IndiaSchool of Computer Science and Engineering, IILM University, Greater Noida 201306, IndiaPVC Academic, Amity University, Noida 201301, IndiaBrain tumor diagnosis is a complex task due to the intricate anatomy of the brain and the heterogeneity of tumors. While magnetic resonance imaging (MRI) is commonly used for brain imaging, accurately detecting brain tumors remains challenging. This study aims to enhance brain tumor classification via deep transfer learning architectures using fine-tuned transfer learning, an advanced approach within artificial intelligence. Deep learning methods facilitate the analysis of high-dimensional MRI data, automating the feature extraction process crucial for precise diagnoses. In this research, several transfer learning models, including InceptionResNetV2, VGG19, Xception, and MobileNetV2, were employed to improve the accuracy of tumor detection. The dataset, sourced from Kaggle, contains tumor and non-tumor images. To mitigate class imbalance, image augmentation techniques were applied. The models were pre-trained on extensive datasets and fine-tuned to recognize specific features in MRI brain images, allowing for improved classification of tumor versus non-tumor images. The experimental results show that the Xception model outperformed other architectures, achieving an accuracy of 96.11%. This result underscores its capability in high-precision brain tumor detection. The study concludes that fine-tuned deep transfer learning architectures, particularly Xception, significantly improve the accuracy and efficiency of brain tumor diagnosis. These findings demonstrate the potential of using advanced AI models to support clinical decision making, leading to more reliable diagnoses and improved patient outcomes.https://www.mdpi.com/2075-1729/15/3/327brain tumorimage processingaugmentationdeep learningtransfer learningfine-tune |
| spellingShingle | Deependra Rastogi Prashant Johri Massimo Donelli Lalit Kumar Shantanu Bindewari Abhinav Raghav Sunil Kumar Khatri Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks Life brain tumor image processing augmentation deep learning transfer learning fine-tune |
| title | Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks |
| title_full | Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks |
| title_fullStr | Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks |
| title_full_unstemmed | Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks |
| title_short | Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks |
| title_sort | brain tumor detection and prediction in mri images utilizing a fine tuned transfer learning model integrated within deep learning frameworks |
| topic | brain tumor image processing augmentation deep learning transfer learning fine-tune |
| url | https://www.mdpi.com/2075-1729/15/3/327 |
| work_keys_str_mv | AT deependrarastogi braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks AT prashantjohri braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks AT massimodonelli braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks AT lalitkumar braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks AT shantanubindewari braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks AT abhinavraghav braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks AT sunilkumarkhatri braintumordetectionandpredictioninmriimagesutilizingafinetunedtransferlearningmodelintegratedwithindeeplearningframeworks |