Diagnosis and precise localization of cardiomegaly disease using U-NET. (2020)

• Record Type: Journal Article
• Authors: Bouslama, Abdelilah; Laaziz, Yassin; Tali, Abdelhak
• Is Part Of: Informatics in medicine unlocked. Volume 19(2020)
• Publication Date: 2020
• Subjects: CXR -- Cardiomegaly -- CNN -- AHE -- U-Net; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100306
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: This study examines an end-to-end technique which uses a Deep Convolutional Neural Network U-Net based architecture to detect Cardiomegaly disease. The learning phase is achieved by using Chest X-ray images extracted from the "ChestX-ray8" open source medical dataset. The Adaptive Histogram Equalization (AHE) method is deployed to enhance the contrast and brightness of the original images. These latter are compressed before undergoing a training stage to optimize computation time. By this method, we obtained a diagnostic accuracy greater than 93%, which outperforms published results for recognizing Cardiomegaly disease. In addition, with U-Net, precise localization of Cardiomegaly is possible, which is not the case in previous works.