Page 107 - The-5th-MCAIT2021-eProceeding
P. 107
Acknowledgements
The authors would like to thank Universiti Kebangsaan Malaysia for providing financial support under the
“Geran Universiti Penyelidikan” research grant, GUP-2020-064.
References
Arahusky.(2021).Tensorflow-Segmentation. Retrieved from https://github.com/arahusky/ Tensorflow-
Segmentation.
Bulla, P., Anantha, L., & Peram, S. (2020). Deep Neural Networks with Transfer Learning Model for Brain
Tumors Classification. Traitement du Signal, 37(4).
Beckett, A. J., Dadakova, T., Townsend, J., Huber, L., Park, S., & Feinberg, D. A. (2020). Comparison of
BOLD and CBV using 3D EPI and 3D GRASE for cortical layer functional MRI at 7 T. Magnetic resonance
in medicine, 84(6), 3128-3145.
Damodharan, S., & Raghavan, D. (2015). Combining tissue segmentation and neural network for brain tumor
detection. International Arab Journal of Information Technology (IAJIT), 12(1).
Deepak, S., & Ameer, P. (2020). MSG-GAN Based Synthesis of Brain MRI with Meningioma for Data
Augmentation.IEEE International Conference on Electronics, Computing and Communication Technologies
(CONECCT).
Divya, S., Suresh, L. P., & John, A. (2020). A Deep Transfer Learning framework for Multi Class Brain Tumor
Classification using MRI. 2nd International Conference on Advances in Computing, Communication Control
and Networking (ICACCCN).
Ide, M., Atsumi, T., Chakrabarty, M., Yaguchi, A., Umesawa, Y., Fukatsu, R., & Wada, M. (2020). Neural
basis of extremely high temporal sensitivity: insights from a patient with autism. Frontiers in Neuroscience, 14,
340.
Mahayuddin, Z. R., & Saif, A. S. (2020). A Comprehensive Review Towards Segmentation And Detection Of
Cancer Cell And Tumor For Dynamic 3d Reconstruction. Asia-Pacific Journal of Information Technology and
Multimedia, 9(1), 28-39.
Müller, S., Weickert, J., & Graf, N. (2016). Automatic brain tumor segmentation with a fast Mumford-Shah
algorithm. Medical Imaging 2016: Image Processing.
Pizer, S. M. (1990). Contrast-limited adaptive histogram equalization: Speed and effectiveness stephen m. pizer,
r. eugene johnston, james p. ericksen, bonnie c. yankaskas, keith e. muller medical image display research
group. First Conference on Visualization in Biomedical Computing, Atlanta, Georgia.
Rathi, V. G. P., & Palani, S. (2015). Brain tumor detection and classification using deep learning classifier on
MRI images. Research Journal of Applied Sciences, Engineering and Technology, 10(2), 177-187.
Saif, A. S., & Mahayuddin, Z. R. (2020). Vehicle Detection for Collision Avoidance Using Vision based
Approach: A Constructive Review. Solid State Technology, 63(2s), 2861-2869.
Saif, A. F. M. S., & Prabuwono, A. S. (2015). Moment Feature Based Fast Feature Extraction Algorithm for
Moving Object Detection Using Aerial Images. PLoS One, 10(6).
Saif, A. F. M. S., Mahayuddin, Z. R., & Prabuwono, A. S. (2015). Efficiency Measurement of Various Denoise
Techniques for Moving Object Detection Using Aerial Images. International Conference on Electrical
Engineering and Informatics (ICEEI).
Saif, A. F. M. S., Prabuwono, A. S., & Mahayuddin, Z. R. (2014). Moving object detection using dynamic
motion modeling from UAV aerial images. Scientific World Journal, 2014.
Schawkat, K., Ciritsis, A., von Ulmenstein, S., Honcharova-Biletska, H., Jüngst, C., Weber, A., . . . Reiner, C.
S. (2020). Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in
MRI: correlation with MR elastography and histopathology. European radiology, 30(8), 4675-4685.
E- Proceedings of The 5th International Multi-Conference on Artificial Intelligence Technology (MCAIT 2021) [94]
Artificial Intelligence in the 4th Industrial Revolution