# Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation

arXiv: https://arxiv.org/abs/2305.05321 · HTML: https://mlsanity.com/paper/2305.05321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 8 of 8)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Addressing materials' microstructure diversity using transfer learning](https://mlsanity.com/paper/2107.13841/) (2021)

## Abstract (arXiv)

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.