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| http://dx.doi.org/10.5001/omj.2023.92 | DOI Listing |
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Department of Information Technology, Aylol University College, Yarim 547, Yemen.
Background/objectives: Chest disease identification for Tuberculosis and Pneumonia diseases presents diagnostic challenges due to overlapping radiographic features and the limited availability of expert radiologists, especially in developing countries. The present study aims to address these challenges by developing a Computer-Aided Diagnosis (CAD) system to provide consistent and objective analyses of chest X-ray images, thereby reducing potential human error. By leveraging the complementary strengths of convolutional neural networks (CNNs) and vision transformers (ViTs), we propose a hybrid model for the accurate detection of Tuberculosis and for distinguishing between Tuberculosis and Pneumonia.
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Department of Ophthalmology, Minto Ophthalmic Hospital, Regional Institute of Ophthalmology, Bangalore Medical College and Research Institute, Bengaluru, Karnataka, India.
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View Article and Find Full Text PDFAnterior segment ischemia following scleral buckle surgery: The forgotten enemy.
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View Article and Find Full Text PDFMicrobial keratitis at a single tertiary eye care in Andhra Pradesh: A 12-year analysis of microbiological profile and susceptibility of bacterial isolates.
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