BRAIN NET: HYBRID TRANSFER MACHINE LEARNING MODEL FOR BRAIN HEMORRHAGE DETECTION

Authors

  • G Vidyulatha Assistance Professor, Department of CSE, Sree Dattha Institute of Engineering & Science Author
  • Kondi Navya Assistance Professor, Department of CSE, Sree Dattha Institute of Engineering & Science Author
  • Rajitha Sapavath Assistance Professor, Department of CSE, Sree Dattha Institute of Engineering & Science Author

Keywords:

Early Detection, Transfer Learning Models, Deep Learning, Medical Image Data, Healthcare, VGG16, VGG19

Abstract

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. 

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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.

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Published

2022-02-28

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Articles

How to Cite

Vidyulatha, G., Navya, K., & Sapavath, R. (2022). BRAIN NET: HYBRID TRANSFER MACHINE LEARNING MODEL FOR BRAIN HEMORRHAGE DETECTION. History of Medicine, 8(1). https://historymedjournal.com/HOM/index.php/medicine/article/view/365