Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem. (August 2021)

• Record Type: Journal Article
• Authors: Davoudi, Khatereh; Thulasiraman, Parimala
• Is Part Of: Simulation. Volume 97:Number 8(2021)
• Page Start: 511
• Page End: 527
• Publication Date: 2021-08
• Subjects: Evolutionary machine learning -- breast cancer -- computer-aided diagnosis systems -- deep learning -- convolutional neural network -- back-propagation -- genetic algorithm; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549721996031
• Languages: English
• ISSNs: 0037-5497

Abstract

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer mortality in women around the world. However, it can be controlled effectively by early diagnosis, followed by effective treatment. Clinical specialists take the advantages of computer-aided diagnosis (CAD) systems to make their diagnosis as accurate as possible. Deep learning techniques, such as the convolutional neural network (CNN), due to their classification capabilities on learned feature methods and ability of working with complex images, have been widely adopted in CAD systems. The parameters of the network, including the weights of the convolution filters and the weights of the fully connected layers, play a crucial role in the classification accuracy of any CNN model. The back-propagation technique is the most frequently used approach for training the CNN. However, this technique has some disadvantages, such as getting stuck in local minima. In this study, we propose to optimize the weights of the CNN using the genetic algorithm (GA). The work consists of designing a CNN model to facilitate the classification process, training the model using three different optimizers (mini-batch gradient descent, Adam, and GA), and evaluating the model through various experiments on the BreakHis dataset. We show that the CNN model trained through the GA performs as well as the Adam optimizer with a classification accuracy of 85%.