BRAIN NET: HYBRID TRANSFER MACHINE LEARNING MODEL FOR BRAIN HEMORRHAGE DETECTION
Keywords:
Early Detection, Transfer Learning Models, Deep Learning, Medical Image Data, Healthcare, VGG16, VGG19Abstract
This research delves into the critical realm of early detection for Alzheimer's disease and brain tumors, two debilitating neurological conditions affecting millions worldwide. The conventional diagnostic methods, reliant on visual inspection and expert interpretation, are time-consuming, subjective, and potentially error prone. To address this challenge, this research explores the application of transfer learning models, specifically deep learning algorithms, to automate and enhance the detection process. In addition, the need for accurate and swift diagnosis has fuelled the development of machine learning models capable of processing extensive medical image data, particularly MRI scans. Transfer learning, a technique leveraging pre-trained deep learning models, offers a promising avenue to overcome the complexities of these diseases. By harnessing the knowledge encoded in large datasets, these models can efficiently identify intricate patterns and abnormalities indicative of Alzheimer's disease and various types of brain tumors. In essence, this research underscores the transformative impact of transfer learning models on Alzheimer's disease and brain tumor detection. By amalgamating advanced machine learning techniques with medical imaging, this approach holds the promise to revolutionize healthcare delivery, offering new horizons for early intervention, improved patient care, and a more efficient healthcare system.
Downloads
References
Hon, M.; Khan, N.M. Towards Alzheimer’s disease classification through transfer learning.
In Proceedings of the 2017 IEEE International Conference on Bioinformatics and
Biomedicine (BIBM), Kansas, MO, USA, 13–16 November 2017; pp. 1166–1169.
Sarraf, S.; Tofighi, G. Deep learning-based pipeline to recognize Alzheimer’s disease using
fMRI data. In Proceedings of the IEEE 2016 Future Technologies Conference (FTC), San
Francisco, CA, USA, 6–7 December 2016; pp. 816–820.
Liu, S.; Liu, S.; Cai, W.; Che, H.; Pujol, S.; Kikinis, R.; Feng, D.; Fulham, M.J. Multimodal
neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans.
Biomed. Eng. 2014, 62, 1132–1140.
Tufail, A.B.; Ma, Y.K.; Zhang, Q.N. Binary classification of Alzheimer’s disease using sMRI
imaging modality and deep learning. J. Digit. Imaging 2020, 33, 1073–1090.
Liu, S.; Liu, S.; Cai, W.; Pujol, S.; Kikinis, R.; Feng, D. Early diagnosis of Alzheimer’s
disease with deep learning. In Proceedings of the 2014 IEEE 11th International Symposium
on Biomedical Imaging (ISBI), Beijing, China, 29 April–2 May 2014; pp. 1015–1018.
Maqsood, M.; Nazir, F.; Khan, U.; Aadil, F.; Jamal, H.; Mehmood, I.; Song, O.y. Transfer
learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI
scans. Sensors 2019, 19, 2645.
Mehmood, A.; Maqsood, M.; Bashir, M.; Shuyuan, Y. A deep Siamese convolution neural
network for multi-class classification of Alzheimer disease. Brain Sci. 2020, 10, 84.
Mehmood, A.; Yang, S.; Feng, Z.; Wang, M.; Ahmad, A.S.; Khan, R.; Maqsood, M.; Yaqub,
M. A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI
images. Neuroscience 2021, 460, 43–52.
Qiu, S.; Joshi, P.S.; Miller, M.I.; Xue, C.; Zhou, X.; Karjadi, C.; Chang, G.H.; Joshi, A.S.;
Dwyer, B.; Zhu, S.; et al. Development and validation of an interpretable deep learning
framework for Alzheimer’s disease classification. Brain 2020, 143, 1920–1933.
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
