A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System
This provides a cheap and early detection system for infectious diseases like pandemics, but it is incremental as it builds on existing CNN models.
The paper tackled the problem of automated lung X-ray abnormality detection by developing a new method called V-BreathNet, which achieved over 96% accuracy, addressing overfitting issues in existing CNN models on black-and-white images.
Introduction: Automated Lung X-Ray Abnormality Detection System is the application which distinguish the normal x-ray images from infected x-ray images and highlight area considered for prediction, with the recent pandemic a need to have a non-conventional method and faster detecting diseases, for which X ray serves the purpose. Obectives: As of current situation any viral disease that is infectious is potential pandemic, so there is need for cheap and early detection system. Methods: This research will help to eases the work of expert to do further analysis. Accuracy of three different preexisting models such as DenseNet, MobileNet and VGG16 were high but models over-fitted primarily due to black and white images. Results: This led to building up new method such as as V-BreathNet which gave more than 96% percent accuracy. Conclusion: Thus, it can be stated that not all state-of art CNN models can be used on B/W images. In conclusion not all state-of-art CNN models can be used on B/W images.