Brain Tumor Detection Using Deep Learning

Abstract

Brain tumor detection is essential for improving patient outcomes through early diagnosis and treatment. Manual interpretation of MRI scans by radiologists can be time-consuming and prone to error. To address this, deep learning techniques, particularly Convolutional Neural Networks (CNNs), offer a promising solution for automated brain tumor detection. This study presents a CNN-based approach that processes MRI scans to detect and classify brain tumors. The model was trained on a dataset of MRI images and optimized using data augmentation and normalization techniques to enhance its generalizability. Performance was evaluated using accuracy, precision, recall, and F1-score, showing the model\'s effectiveness in identifying brain tumors with high accuracy. This automated system can assist healthcare professionals by providing faster and more reliable diagnoses, improving the efficiency and precision of brain tumor detection in clinical settings.

Introduction

Brain tumors are abnormal brain growths that can be benign or malignant, with malignant tumors being more dangerous. Early detection is vital for effective treatment and improved survival rates. Traditional diagnostic methods like MRI and CT scans rely on radiologists’ manual interpretation, which can be error-prone and inconsistent.

Deep learning, especially Convolutional Neural Networks (CNNs), has emerged as a powerful tool for analyzing medical images. CNNs automatically extract features from brain scans, enabling faster and more accurate tumor detection than manual methods. This technology can reduce radiologists’ workload, improve diagnosis speed, and lower false positive/negative rates, thus enhancing patient outcomes. However, challenges remain, such as the need for large labeled datasets, ensuring model generalization, and addressing ethical concerns.

The literature survey highlights multiple studies focusing on brain tumor detection using machine learning and deep learning techniques:

• Comprehensive reviews cover tumor anatomy, datasets, segmentation, and classification methods, including transfer and quantum learning.

• Several approaches use CNNs for segmentation and classification, achieving high accuracy (around 95-99%) on datasets like BraTS.

• Other methods combine CNNs with Support Vector Machines (SVM) and gray-level co-occurrence features for improved tumor localization and classification.

• Advanced architectures like U-Net and 3D CNNs have shown significant improvements in segmentation accuracy and precision.

Overall, deep learning-based models have great potential to revolutionize brain tumor detection by providing reliable, efficient, and accurate tools for clinical use.

Conclusion

The brain tumor detection project utilizing deep learning represents a significant advancement in medical imaging. By leveraging Convolutional Neural Networks (CNNs), the project successfully automates the detection and classification of brain tumors in MRI scans, enhancing diagnostic accuracy and efficiency. This approach addresses critical challenges in early tumor detection and showcases the potential for integrating AI technologies into clinical settings. Although challenges such as ensuring data quality and addressing ethical considerations remain, the project\'s results are promising. Future directions could involve further model refinement, exploring transfer learning, and implementing the system in real-world healthcare environments. Overall, this project lays the groundwork for innovations in brain tumor diagnosis, contributing to improved patient outcomes and advancing the role of AI in medicine.

References

[1] Javaria Amin, Muhammad Sharif, Anandakumar Haldorai, Mussarat Yasmin, Ramesh Sundar Nayak, \" Brain tumor detection and classification using machine learning”, Received july 28, 2021, accepted october 12, 2021, date of publication november 8, 2021 [2] Shubhangi Solanki, Uday Pratap Singh, Siddharth Singh Chouhan and Sanjeev Jain, “Brain Tumor Detection and Classification Using Intelligence Techniquess”, IOSR Journal of Computer Engineering received jan 17,2023, accepted feb 1,2023 date of publication feb 6, 2023 date of current version feb 10,2023 [3] Sai Meghana S, Amulya P, Manish A,Rajarajeswari P, “A Deep Learning Approach For Brain Tumor Segmentation Using Convolution Neural Network”, Received 22 February 2023, accepted 21 March 2023, date of publication 29 March 2023, date of current version 12 April 2023. Digital Object Identifier 10.1109/ACCESS.2023.326304 [4] Mr. T. Sathies Kumar, K. Rashmi, Sreevidya Ramadoss, L. k. Sandhya, T. J. Sangeetha, “Brain Tumor Detection Using SVM Classification”, Received 3 July 2024, accepted 22 July 2024, date of publication 25 July 2024, date of current version 2 August 2024. Digital Object Identifier 10.1109/ACCESS.2024.343349 [5] Surendran Rajendran, Suresh Kumar Rajgopal, Tamilvizhi Thanarajan, K. Shankar, Sachin Kumar, Njah M. Alsubaie, Mohamad Khairi Ishak and Samih M. Mostafa, “The proposed technique uses a Gray Level Co-occurrence matrix extraction of features approach to strip out unwanted details from the images”. [6] Abhishek Anil, Aditya Raj, H. Arvind Sarama, Naveen Chandran R, Deepa P. L., “Brain Tumor Detection from MRI using Deep Learning”, Received 10 June 2024, accepted 11 July 2024, date of publication 15 July 2024, date of current version 26 July 2024. Digital Object Identifier 10.1109/ACCESS.2024.3428553 [7] Hossam H. Sultan, Nancy M. Salem, And Walid Al-Atabany, titled“Multi-Classification of brain tumor images using deep neural network “Leaf disease detection using CNN”, © 2022 IJCRT Volume 10, Issue 3 March 2022 | ISSN: 2320-2882 [8] Hossam H. Sultan, Nancy M. Salem, And Walid Al-Atabany, titled“Multi-Classification of brain tumor images using deep neural network “Leaf disease detection using CNN”, © 2022 IJCRT Volume 10, Issue 3 March 2022 | ISSN: 2320-2882

Copyright

Copyright © 2025 Durvesh Patil , Kalyani Puri, Aasim Shaikh , Prof. Anuradha A Pandit . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.