Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images.

Autor: Al-Sheikh MH; Physiology Department, College of Medicine, Imam Abdulrahman Bin Faisal University, 34212, Dammam, Saudi Arabia., Al Dandan O; Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, 34212, Dammam, Saudi Arabia., Al-Shamayleh AS; Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Al-Salt, Amman, 19328, Jordan., Jalab HA; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nile Street, 64001, Thi-Qar, Iraq. hamid.a@alayen.edu.iq., Ibrahim RW; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nile Street, 64001, Thi-Qar, Iraq.; Department of Mathematics, Mathematics Research Center, Near East University, Near East Boulevard, PC: 99138, Nicosia/Mersin 10, Turkey.; Department of Computer Science and Mathematics, Lebanese American University, Beirut, 1102 2801, Lebanon.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Nov 08; Vol. 13 (1), pp. 19373. Date of Electronic Publication: 2023 Nov 08.
DOI: 10.1038/s41598-023-46147-3
Abstrakt: Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease's spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.
(© 2023. The Author(s).)
Databáze: MEDLINE
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