Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

Abstract

The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial network (I-GAN) is proposed to overcome the class imbalance problem. Here, the proposed method is the consolidation of deep convolutional generative adversarial network (DCIGAN) and modified convolutional neural network, (MCNN), therefore it is known as DCIGAN-MCNN method. First, the DCIGAN is utilized for balancing the dataset by generating more samples in the training dataset. Then, this training dataset based the classification of Breast cancer is developed using the modified convolutional neural network. The proposed method is executed in MATLAB. The performance analysis are carried out in Breast Cancer Wisconsin (Prognostic) Data Set provides Maximum Geometry Mean (MGM) as 24.058%, 9.582%, Matthews’s correlation coefficient (MCC) as 78.623%, 30.357% higher than the existing methods, like CI-BC-RK-SVM, CI-BC-GA, CI-BC-DC-CNN, CI-BC-RF and CI-BC-BMIC-Net respectively. Finally, the simulation results prove that the proposed method can be able to find the optimal solutions efficiently and accurately.