Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Sensors - Tập 22 Số 3 - Trang 807
Kiran Jabeen1, Muhammad Attique Khan1, Majed Alhaisoni2, Usman Tariq3, Yudong Zhang4, Ameer Hamza1, Artūras Mickus5, Robertas Damaševičius5
1Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
2College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia
3College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj 11942, Saudi Arabia
4Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK
5Department of Applied Informatics, Vytautas Magnus University, LT-44404 Kaunas, Lithuania

Tóm tắt

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

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