Enhancement of Chest X-Ray Images Classification With Fuzzy-Variable Neural Network Activation Function

This study presents a novel Variable Single-Input Type-2 Fuzzy Rectifying Units activation function (VAR-SIT2-FRU), incorporating variable triangular membership functions assigned to different input values. It adjusts the width of the membership function dynamically to optimize performance for various tasks. The proposed activation function is designed to capture nonlinear relationships in data and enhance the efficiency and reliability of deep learning models while reducing computational costs compared to traditional activation functions. These make it more appropriate for medical image analysis tasks. The paper focuses on evaluating the performance of VAR-SIT2-FRU against five widely used activation functions and the classic SIT2-FRU activation function using AlexNet and ResNet-50 architectures. The experiments focused on classifying COVID-19, normal, and pneumonia using chest X-ray images. All images are preprocessed, normalized, and augmented to prevent overfitting. The significant results show that VAR-SIT2-FRU is suitable for medical classification tasks. It achieves higher classification accuracy and improved learning efficiency.