HYBRID TRANSFER LEARNING MODELS: A NEW FRONTIER IN EARLY DETECTION OF ALZHEIMER'S DISEASE AND BRAIN TUMORS
DOI:
https://doi.org/10.48047/Keywords:
VGG16, VGG19, Deep Learning, Early Detection, Transfer Learning Models, and Medical Image DataAbstract
This study explores the crucial field of brain tumor and Alzheimer's disease early detection, two crippling neurological disorders that impact millions of people globally. The traditional diagnostic techniques are laborious, subjective, and perhaps prone to error because they rely on visual inspection and professional interpretation. This study investigates the use of transfer learning models—more
especially, deep learning algorithms—to improve and automate the detection process in order to overcome this difficulty.Furthermore, the development of machine learning algorithms that can interpret large amounts of medical imaging data, especially from MRI scans, has been fueled by the demand for quick and accurate diagnosis.
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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.
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