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Predicting Brain Tumor using Transfer Deep Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 37 |
| Year of Publication: 2022 |
| Authors: Mustafa Abdul Salam, Sanaa Taha, Sameh El_ahmdy |
10.5120/ijca2022922445
|
Mustafa Abdul Salam, Sanaa Taha, Sameh El_ahmdy . Predicting Brain Tumor using Transfer Deep Learning. International Journal of Computer Applications. 184, 37 ( Nov 2022), 33-37. DOI=10.5120/ijca2022922445
Abstract
Brain tumor is an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis of this dangerous disease can save lives. Deep learning applications have shown significant improvements in recent years.Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using deep learning techniques has made great strides by providing reliable datasets. In this paper transfer models such as the MobileNet, VGG19, InceptionResNetV2, Inception, and DenseNet201 model is applied to predict brain tumors. The proposed models use three different optimizers, Adam, SGD, and RMSprop. Simulation results show that the pre-trained MobileNet model with RMSprop optimizer outperformed all compared models.It achieved 0.995 accuracy, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational time.
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