  Computer-aided detection has been a research area attracting great interest
in the past decade. Machine learning algorithms have been utilized extensively
for this application as they provide a valuable second opinion to the doctors.
Despite several machine learning models being available for medical imaging
applications, not many have been implemented in the real-world due to the
uninterpretable nature of the decisions made by the network. In this paper, we
investigate the results provided by deep neural networks for the detection of
malaria, diabetic retinopathy, brain tumor, and tuberculosis in different
imaging modalities. We visualize the class activation mappings for all the
applications in order to enhance the understanding of these networks. This type
of visualization, along with the corresponding network performance metrics,
would aid the data science experts in better understanding of their models as
well as assisting doctors in their decision-making process.
