سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

A Deep Learning-based Strategy for Bronchitis Detection in Online Telehealth Platforms

نویسندگان :

Saeideh Mehrabani¹

1- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Deep Learning،Telehealth،Bronchitis،Remote Diagnosis

چکیده :

In recent years, the healthcare sector has witnessed a significant transformation driven by rapid advancements in digital technologies. One of the most notable shifts has been the widespread adoption of telehealth systems, with a particular emphasis on remote diagnostic tools for respiratory conditions such as bronchitis. In light of the growing demand for precise and reliable detection methods, this study introduces an innovative framework named BronchoTeleNet, which leverages well-established pre-trained convolutional neural networks including DenseNet-121, VGG-16, MobileNet, and InceptionV3. The proposed system thoroughly investigates the effectiveness of these models in identifying bronchitis cases through online telemedicine platforms. Model performance is assessed through critical metrics such as accuracy, precision, recall, and F1-score, along with advanced analytical tools like the confusion matrix and ROC curve to offer a comprehensive evaluation. Furthermore, the validation loss and accuracy trends are presented to facilitate a comparative understanding of each model’s capabilities. The BronchoTeleNet framework illustrates a robust and scalable solution aimed at enhancing diagnostic reliability in remote healthcare services focused on bronchitis detection