Lung cancer classification using exponential mean saturation linear unit activation function in various generative adversarial network models

Abstract

Nowadays, the mortality rate due to lung cancer increases rapidly worldwide as it can be classified only at the later stages. Early classification of lung cancer will help patients to take treatment and decrease the death rate. The limited dataset and diversity of data samples are the bottlenecks for early classification. In this paper, robust deep learning generative adversarial network (GAN) models are employed to enhance the dataset and to increase classification accuracy. The activation function plays an important feature-learning role in neural networks. Since the existing activation functions suffer from various drawbacks such as vanishing gradient, dead neurons, output offset, etc., this paper proposes a novel activation function exponential mean saturation linear unit (EMSLU), which aims to speed up training, reduce network running time, and improve classification accuracy. The experiments were conducted using vanilla GAN, Wasserstein generative adversarial network, Wasserstein generative adversarial network with gradient penalty, conditional generative adversarial network, and deep convolutional generative adversarial network. Each GAN is tested with rectified linear unit, exponential linear unit, and proposed EMSLU activation functions. The results show that all the GAN's with EMSLU yields improved precision, recall, F1-score, and accuracy.

PolyLU: A Simple and Robust Polynomial-Based Linear Unit Activation Function for Deep Learning

“…ALKHOULY et al [41] proposed the IpLU and AbsLU without exponential terms to make it simple in computation. Egambaram et al [42] proposed the EMSLU activation function tested by GAN [43] and performed better than ReLU and ELU, which also has more complex exponential terms. The NAF [44] is used in RNN, the performance is slightly better than ELU, but the operation is complex and requires careful selection of parameters.…”