Volume 26, Issue 4 (Autumn 2020)                   Horizon Med Sci 2020, 26(4): 398-413 | Back to browse issues page


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Najaf-Zadeh A, Ghaffari H R. A Two-Dimensional Convolutional Neural Network for Brain Tumor Detection From MRI. Horizon Med Sci. 2020; 26 (4) :398-413
URL: http://hms.gmu.ac.ir/article-1-3432-en.html
1- Department of Engineering Intelligence, Faculty of Engineering, Azad University, Ferdows Branch, Ferdows, Iran.
2- Department of Engineering Intelligence, Faculty of Engineering, Azad University, Ferdows Branch, Ferdows, Iran. , hamidghaffary53@yahoo.com
Abstract:   (483 Views)
Aims: Cancerous brain tumors are among the most dangerous diseases that lower the quality of life of people for many years. Their detection in the early stages paves the way for the proper treatment. The present study aimed to present a two-dimensional Convolutional Neural Network (CNN) for detecting brain tumors under Magnetic Resonance Imaging (MRI) using the deep learning method.
Methods & Materials: The proposed method has two stages of feature extraction and classification. A 12-layer CNN was used to extract the features of the MRI images and then the softmax activation function was used to classify these features. The proposed method was applied to a standard database consisting of three brain tumor types of meningioma, glioma, and pituitary.
Findings: The proposed method had better performance compared to previously presented methods. Its accuracy was reported as 98.68%.
Conclusion: Meningioma, glioma, and pituitary tumors are the most common types of brain tumors. Early detection of these tumors can decrease the risk of death. Because of its fully connected structure, the use of proposed deep CNN can help physicians to correctly detect brain tumors with MRI images.
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Type of Study: Original | Subject: Diseases
Received: 2019/12/7 | Accepted: 2020/06/20 | Published: 2020/09/30

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