Detection of Pneumonia from X-Ray Imaging using Faster RCNN
Pneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process. Generally, the disease can be diagnosed from chest X-ray images by an expert radiologist. The diagnoses can be subjective for some reasons such as the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Therefore, computer-aided diagnosis systems are needed to guide the clinicians. In the division of health care artificial intelligence plays a vital role to perform better diagnosis with less error rate. Deep learning techniques can be employed to predict the disease with higher accuracy. Previously a comparative analysis was made on Xception vs vgg16 and CNN(Convolutional Neural Network) VGG19, ResNet, Inception V3. The main disadvantage of CNN is that it doesn’t encode the position and orientation of object. In order to overcome the existing disadvantage the proposed system was compiled using Faster RCNN (Region Convolutional Neural network). In RCNN method selective search is done based on the bounding box values. The system has been trained and tested with the 10000 images of x-ray dataset and it shows a higher accuracy when compared to the existing models.