Deep Transfer Learning for COVID 19 Prediction: Case Study for Limited Data Problems.
OBJECTIVE Automatic prediction of COVID 19 using deep convolution neural networks based pre trained transfer models and Chest X ray images. METHOD This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID 19 from X ray images as compared to diagnosis performed by experts in the medical community. RESULT Due to the fact that the dataset available for COVID 19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over fit when data augmentation is not used, this is due to the small amount of data used for training and validation. CONCLUSION A deep transfer learning is proposed to detecting the COVID 19 automatically from chest X ray by training it with X ray images gotten from both COVID 19 patients and people with normal chest Xrays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID 19.
Current medical imaging