Unleashing Modified Deep Learning Models in Efficient COVID19 Detection

• Main Authors: Islam, Md Aminul, Shuvo, Shabbir Ahmed, Rony, Mohammad Abu Tareq, Raihan, M, Sufian, Md Abu
• Format: Journal Article
• Language: English
• Published: 21-10-2023
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2310.14081

The COVID19 pandemic, a unique and devastating respiratory disease outbreak,
has affected global populations as the disease spreads rapidly. Recent Deep
Learning breakthroughs may improve COVID19 prediction and forecasting as a tool
of precise and fast detection, however, current methods are still being
examined to achieve higher accuracy and precision. This study analyzed the
collection contained 8055 CT image samples, 5427 of which were COVID cases and
2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non
COVID cases. The most accurate models are MobileNet V3 (97.872 percent),
DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High
accuracy indicates that these models can make many accurate predictions, as
well as others, are also high for MobileNetV3 and DenseNet201. An extensive
evaluation using accuracy, precision, and recall allows a comprehensive
comparison to improve predictive models by combining loss optimization with
scalable batch normalization in this study. Our analysis shows that these
tactics improve model performance and resilience for advancing COVID19
prediction and detection and shows how Deep Learning can improve disease
handling. The methods we suggest would strengthen healthcare systems,
policymakers, and researchers to make educated decisions to reduce COVID19 and
other contagious diseases.
CCS CONCEPTS Covid,Deep Learning, Image Processing
KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet,
GoogleNet, Image Processing, Disease Detection.