Efficient and Fast Kolmogorov–Arnold Networks for Chest X-Ray Classification of Pneumonia and Tuberculosis

Interpreting chest X-ray images can be challenging, leading to an increasing reliance on computational methods by physicians to improve diagnostic accuracy and disease monitoring. Among these methods, machine learning models—particularly those in computer vision, such as Convolutional Neural Networks (CNNs)—have shown strong performance in image classification tasks. Recently, Kolmogorov-Arnold Networks (KANs) have emerged as an alternative framework that redefines how connections and activations are computed within neural architectures. This study aims to develop a classification system using a dataset of 5,000 chest X-ray images categorized as Normal, Pneumonia, or Tuberculosis. Two KAN-based algorithms, Efficient KAN (EKAN) and Fast KAN (FKAN), were implemented and compared with a CNN baseline. Experimental results show that the FKAN model achieved performance comparable to CNN with approximately 96% accuracy in test cases, but with a reduced computational cost of approximately 96.18%. The results suggest that the FKAN and EKAN algorithms are a practical and efficient approach for medical image classification.