Automatic breast cancer detection based on optimized neural network using whale optimization algorithm. Issue 1 (12th August 2020)

• Record Type: Journal Article
• Authors: Fang, Hong; Fan, Hongyu; Lin, Shan; Qing, Zhang; Sheykhahmad, Fatima Rashid
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 1(2021)
• Page Start: 425
• Page End: 438
• Publication Date: 2020-08-12
• Subjects: breast cancer -- computer‐aided diagnosis -- image segmentation -- neural networks -- whale optimization algorithm; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22468
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high‐accuracy computing, computer‐aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.