  Biomedical images are increasing drastically. Along the way, many machine
learning algorithms have been proposed to predict and identify various kinds of
diseases. One such disease is Pneumonia which is an infection caused by both
bacteria and viruses through the inflammation of a person's lung air sacs. In
this paper, an algorithm was proposed that receives x-ray images as input and
verifies whether this patient is infected by Pneumonia as well as specific
region of the lungs that the inflammation has occurred at. The algorithm is
based on the transfer learning mechanism where pre-trained ResNet-50
(Convolutional Neural Network) was used followed by some custom layer for
making the prediction. The model has achieved an accuracy of 90.6 percent which
confirms that the model is effective and can be implemented for the detection
of Pneumonia in patients. Furthermore, a class activation map is used for the
detection of the infected region in the lungs. Also, PneuNet was developed so
that users can access more easily and use the services.
