Application of Artificial Intelligence in the Classification of
  Microscopical Starch Images for Drug Formulation
Marvellous Ajala Blessing Oko David Oba-Fidelis Joycelyn Iyasele Joy I. Odimegwu
Starches are important energy sources found in plants with many uses in the
pharmaceutical industry such as binders, disintegrants, bulking agents in drugs
and thus require very careful physicochemical analysis for proper
identification and verification which includes microscopy. In this work, we
applied artificial intelligence techniques (using transfer learning and deep
convolution neural network CNNs to microscopical images obtained from 9 starch
samples of different botanical sources. Our approach obtained an accuracy of
61% when the machine learning model was pretrained on microscopic images from
MicroNet dataset. However the accuracy jumped to 81% for model pretrained on
random day to day images obtained from Imagenet dataset. The model pretrained
on the imagenet dataset also showed a better precision, recall and f1 score
than that pretrained on the imagenet dataset.