Revolutionizing Covid-۱۹ Diagnosis: The Impact of Automated Chest X-ray Analysis through Deep Learning

Using cutting-edge technology, this groundbreaking study developed a novel approach to diagnosing COVID-۱۹. By utilizing wavelet transformation and fuzzy logic, we have successfully removed noise from CT images, enabling us to accurately segment lung regions. Our innovative approach combines global and local threshold methods, resulting in unparalleled success in segmenting lung images. We have further employed state-of-the-art techniques such as AlexNet for feature extraction and Support Vector Machine (SVM) for classification, achieving an astonishing ۹۹.۸% accuracy in classifying COVID-۱۹, Viral Pneumonia, and Normal data. Our method outperforms previous approaches and represents a significant breakthrough in medical diagnosis.

Keywords:

Convolutional neural networks , COVID-۱۹ , AlexNet , Support vector machine (SVM) , lung segmentation.