A self-trained Convolutional Neural Network (CNN) has been developed for the diagnosis of tuberculosis using medical imaging. The CNN was trained on a dataset of chest X-rays and achieved high accuracy in detecting the disease. This technology has the potential to improve the efficiency and accuracy of tuberculosis diagnosis, especially in resource-limited settings where access to trained radiologists may be limited. The CNN was able to accurately identify tuberculosis cases and distinguish them from other conditions, making it a promising tool for early detection and treatment of the disease. Further research and validation of this technology are needed to ensure its effectiveness and reliability in real-world clinical settings.
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Source link: https://www.cureus.com/articles/248945-self-trained-convolutional-neural-network-cnn-for-tuberculosis-diagnosis-in-medical-imaging?score_article=true
#SelfTrainedCNN for Tuberculosis Diagnosis in Medical Imaging – Cureus #MedicalImagingTuberculosisDetection
