Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images

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Hamed Behzadi Khormuji, Habib Rostami, Sana Salehi, Touba Derakhshande-Rishehri, Marzieh Masoumi, Siavash Salemi, Ahmad Keshavarz, Ali Gholamrezanezhad, Majid Assadi, Ali Batouli

Published: 01 Jan 2020, Last Modified: 29 Sept 2024Comput. Methods Programs Biomed. 2020EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•ChestNet is proposed which is proportional to the size of the dataset for detecting consolidation in chest X-ray images.•ChestNet has two times fewer max-pooling layers than the VGG16 and DenseNet121 to preserves the features of the images.•An efficient pre-processing process is proposed to remove confounding variables and histogram difference between images.•An extra validation with a totally different dataset is performed to indicate the generality of the proposed model.