Effsemble: faster, smaller and more accurate ensemble networks for thoracic disease classification
by Arren Matthew C. Antioquia
International Journal of Computer Applications in Technology (IJCAT), Vol. 71, No. 4, 2023

Abstract: Convolutional Neural Networks (CNNs) are being adapted to various computer-aided diagnosis applications, including recognising thoracic diseases. To improve classification performance, recent solutions alter the structure of existing networks or require additional prior information for training. Other approaches demand massive computational requirement to increase classification accuracy. In this paper, we propose a family of efficient ensemble networks called Effsemble to accurately recognise thoracic diseases without additional layers, extra input data, or large computational overhead. Our proposed approach achieves the highest average AUROC score of 80.04% in the ChestX-ray14 data set. Moreover, our Effsemble-2 outperforms other state-of-the-art methods, while decreasing the parameter count by 31.7M and increasing inference speed by 2.75x compared to the previous best ensemble.

Online publication date: Wed, 19-Jul-2023

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