Drug Analyser Using Neural Networks with the Use of Transfer Learning Techniques

Abstract

ResNet architecture was used to create a user-friendly drug analyzer web application. This architecture is a transfer learning method that was used as a convolutional neural network in this case, and it will be trained on a collection of images that contain labels for each drug individually. The activation functions used within these neural networks are ReLU (Rectified Linear Unit) and softmax activation functions, as well as categorical cross-entropy as a loss function. Stochastic gradient descent (adam optimizer) was used to change the weights for each input on each epoch. Finally, after receiving a traditional model, it was merged with a web application API such as Flask in Python. After that, the web application was deployed to cloud platforms such as Heroku.