CovNet-UFCSPA: auxiliando no diagnóstico de pneumonia por coronavírus

Objective: This study introduces the CovNet-UFCSPA architecture, which incorporates pre-processing data from clinical images (X-rays) and deep learning algorithms. Method: A total of 24,235 images were used for training, validation, and testing of the model, identifying areas in the X-rays that influence the model's decision. Result: The architecture achieved a recall of 99% in classifying X-rays from patients at the Hospital de Clínicas de Porto Alegre (HCPA). The application of the CLAHE technique improved the region of interest in the X-rays, reducing the false negative rate from 187 to 9. Conclusion: Compared with Resnet50 V2 and Inception V3 architectures, CovNet-UFCSPA demonstrated superiority in false negative rates, true positives, and recall.