دانشگاه آزاد اسلامی واحد تهران جنوب

صفحه اصلی / سومین کنفرانس ملی فناوری های نوین در انرژی

Optimizing Energy and Time for Powered Rapid and Accurate COVID-19 Patients Detection

نویسندگان :

Neda Sefandarmaz¹ Hasan Ghalami Bavil Olyaee² Neda Gorgi Kandi³

1- دانشگاه آزاد اسلامی واحد تهران جنوب 2- دانشگاه آزاد اسلامی واحد تهران جنوب 3- دانشگاه آزاد اسلامی واحد تهران جنوب

کلمات کلیدی :

COVID 19،Classification،X-Ray Image،CNN

چکیده :

The healthcare systems have been greatly affected by the COVID 19 pandemic highlighting the need, for accurate diagnosis of COVID 19 patients. This study focuses on using chest X rays to achieve this goal. The researchers suggest utilizing CNN models, a form of intelligence to automate the diagnostic process. While an initial model showed accuracy (99.5%) issues with data quality were noted. To overcome this they implemented techniques such as data balancing, expert evaluation and data enhancement. These enhancements resulted in an final model. The researchers investigated the use of CNNs to detect COVID 19 from chest X rays automatically. Despite a high accuracy rate (99.5%) refinements were made due to identified limitations. Through model optimization significant performance improvements were achieved. Early and precise diagnosis of COVID 19 is crucial. Chest X ray data poses challenges for machine learning models. Lightweight CNNs provide a solution for automated detection, with enhanced data quality through processing techniques. Determining the number of layers is essential to prevent overfitting, which was addressed by the researchers leading to model enhancement. This study emphasizes the significance of development and optimization to maximize machine learning model performance.