Detection and Segmentation of Breast Cancer Using Auto Encoder Deep Neural Networks
Subject Areas : Majlesi Journal of Telecommunication Devices
1 - Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
2 - Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Iran.
Keywords: Segmentation, Automatic encoder neural networks, Ultrasound images, Breast masses,
Abstract :
Breast cancer is the most common type of cancer among women worldwide. If diagnosed by a doctor in the early stages, it can save the patient's life. Ultrasound imaging is one of the most widely used diagnostic tools for diagnosing and classifying breast abnormalities. However, accurate segmentation of the ultrasound image is a challenging problem due to the artifacts created on the ultrasound image. Although deep learning-based methods have been able to overcome some of these challenges, the accuracy of tumor region detection in this image is still low. In this paper, we have proposed approaches for breast ultrasound image segmentation based on auto-encoder deep neural network. The proposed method has two parts. The classification section to determine the image with cancerous tissue and the tumor segmentation section to segment the desired area. which will be shown in the network output of the encoder itself. The proposed method has been evaluated qualitatively and quantitatively. The superiority of the proposed method with accuracy and dice criteria is 89 and 90 percent, respectively which shows the effectiveness of this method in diagnosis.
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