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Brain tumor diagnoses based on VGG-16 and MobileNet
- 1 Tongji University
* Author to whom correspondence should be addressed.
Abstract
Brain tumor, recognized as one of the most formidable and aggressive diseases globally, continues to pose significant challenges for medical practitioners in clinical diagnosis and treatment. Addressing the burden on doctors and addressing resource limitations in certain hospitals necessitates the development of efficient and dependable alternative approaches. Convolutional Neural Networks (CNNs), renowned for their prowess in image recognition, hold immense potential in addressing this pressing issue. Leveraging transfer learning, the capabilities of established models such as VGG-16 and MobileNet can be harnessed to construct superior models within a comparatively abbreviated timeframe. This research paper aims to construct and evaluate VGG-16 and MobileNet-based models, employing transfer learning, to explore the applicability of these two classical models in the context of brain tumor diagnosis. The ultimate goal is to assist doctors and hospitals in alleviating the challenges associated with brain tumor diagnoses. The results demonstrated the effectiveness of brain tumor recognition based on CNNs.
Keywords
brain tumor, CNN, transfer learning, VGG-16, MobileNet
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Cite this article
Liu,Y. (2023). Brain tumor diagnoses based on VGG-16 and MobileNet. Applied and Computational Engineering,22,28-34.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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