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The COVID19 pandemic, a unique and devastating respiratory disease outbreak,has affected global populations as the disease spreads rapidly. Recent DeepLearning breakthroughs may improve COVID19 prediction and forecasting as a toolof precise and fast detection, however, current methods are still beingexamined to achieve higher accuracy and precision. This study analyzed thecollection contained 8055 CT image samples, 5427 of which were COVID cases and2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 nonCOVID cases. The most accurate models are MobileNet V3 (97.872 percent),DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). Highaccuracy indicates that these models can make many accurate predictions, aswell as others, are also high for MobileNetV3 and DenseNet201. An extensiveevaluation using accuracy, precision, and recall allows a comprehensivecomparison to improve predictive models by combining loss optimization withscalable batch normalization in this study. Our analysis shows that thesetactics improve model performance and resilience for advancing COVID19prediction and detection and shows how Deep Learning can improve diseasehandling. The methods we suggest would strengthen healthcare systems,policymakers, and researchers to make educated decisions to reduce COVID19 andother contagious diseases.  CCS CONCEPTS Covid,Deep Learning, Image Processing  KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet,GoogleNet, Image Processing, Disease Detection.

Unleashing Modified Deep Learning Models in Efficient COVID19 Detection