  The cost of some drugs and medical treatments has risen in recent years that
many patients are having to go without. A classification project could make
researchers more efficient.
  One of the more surprising reasons behind the cost is how long it takes to
bring new treatments to market. Despite improvements in technology and science,
research and development continues to lag. In fact, finding new treatment
takes, on average, more than 10 years and costs hundreds of millions of
dollars. In turn, greatly decreasing the cost of treatments can make ensure
these treatments get to patients faster. This work aims at solving a part of
this problem by creating a cellular image classification model which can
decipher the genetic perturbations in cell (occurring naturally or
artificially). Another interesting question addressed is what makes the
deep-learning model decide in a particular fashion, which can further help in
demystifying the mechanism of action of certain perturbations and paves a way
towards the explainability of the deep-learning model.
  We show the results of Grad-CAM visualizations and make a case for the
significance of certain features over others. Further we discuss how these
significant features are pivotal in extracting useful diagnostic information
from the deep-learning model.
