2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 2 , PP: 34-41, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ehsan khodadadi 1 * , S. K. Towfek 2 , Hussein Alkattan 3
Doi: https://doi.org/10.54216/FPA.130203
Convolutional Neural Networks (CNNs) are the most popular neural network model for the image classification problem, which has seen a surge in interest in recent years thanks to its potential to improve medical picture classification. CNN employs a number of building pieces, including convolution layers, pooling layers, and fully connected layers, to determine features in an adaptive manner via backpropagation. In this study, we aimed to create a CNN model that could identify and categorize brain cancers in T1-weighted contrast-enhanced MRI scans. There are two main phases to the proposed system. To identify images using CNN, first they must be preprocessed using a variety of image processing techniques. A total of 3064 photos of glioma, meningioma, and pituitary tumors are used in the investigation. Testing accuracy for our CNN model was 94.39%, precision was 93.33%, and recall was 93% on average. The suggested system outperformed numerous well-known current algorithms and demonstrated satisfactory accuracy on the dataset. We have performed several procedures on the data set to get it ready for usage, including standardizing the pixel sizes of the photos and dividing the dataset into 80% for train, 10% for test, and 10% for validation. The proposed classifier achieves a high level of accuracy of 95.3%.
Brain Tumor , Convolutional Neural Networks , Kernel , Histogram Equalization , Feature Maps , Adam Optimization.
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