Convolutional neural networks for the classification of chest X-rays in the IoT era

Khaled Almezhghwi; Sertan Serte; Fadi Al-Turjman

doi:10.1007/s11042-021-10907-y

Convolutional neural networks for the classification of chest X-rays in the IoT era

Multimed Tools Appl. 2021;80(19):29051-29065. doi: 10.1007/s11042-021-10907-y. Epub 2021 Jun 17.

Authors

Khaled Almezhghwi¹, Sertan Serte², Fadi Al-Turjman^{3

4}

Affiliations

¹ Electrical and Electronic Engineering, College Of Electronic Technology, Tripoli, Libya.
² Electrical and Electronic Engineering, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey.
³ Artificial Intelligence Department, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey.
⁴ Research Center for AI and IoT, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey.

Abstract

Chest X-ray medical imaging technology allows the diagnosis of many lung diseases. It is known that this technology is frequently used in hospitals, and it is the most accurate way of detecting most thorax diseases. Radiologists examine these images to identify lung diseases; however, this process can require some time. In contrast, an automated artificial intelligence system could help radiologists detect lung diseases more accurately and faster. Therefore, we propose two artificial intelligence approaches for processing and identifying chest X-ray images to detect chest diseases from such images. We introduce two novel deep learning methods for fast and automated classification of chest X-ray images. First, we propose the use of support vector machines based on the AlexNet model. Second, we develop support vector machines based on the VGGNet16 method. Combined deep networks with a robust classifier have shown that the proposed methods outperform AlexNet and VGG16 deep learning approaches for the chest X-ray image classification tasks. The proposed AlexNet and VGGNet based SVM provide average area under the curve values of 98% and 97%, respectively, for twelve chest X-ray diseases.

Keywords: Convolutional neural networks; Deep learning.