Build 2 models ( No. 1. transfer learning- completed but to be adjusted and No.2. CNN- to be done ) for comparison

General instruction: You should do transfer learning first ( to mend code available on github -accuracy is 94% ) and then you should develop&train your enhanced CNN.
Then you should do a comparative analysis of both model.

Questions I will be having for you when you are catering for the below in your python codes?
-Application of pre-trained model
-Development of enhanced CNN
-Explanation on training set and testing set
-How you have trained the model and solve the issues of overfitting
-There are very little difference between different abnormalities. How you cater for that?
-What are the parameters that influence the performance
-Detailed description on performance and evaluation
-In medical field, GAN is being used instead of data augmentation -What you do in case of many unlabeled data?


Reference No.1 for Transfer Learning:
Tutorial: https://www.youtube.com/watch?v=3K2E7eppaZQ watch from 33:15 to 43:15
Github code: https://github.com/neilkach/NASAJr_BWSI_Final_WebApp

Reference No.2 for Transfer Learning:
Github code: https://github.com/lkampat/Lung_cancer_Histopathological-Images_Classification

Reference No.3:
Github code: https://github.com/akrlowicz/lung-cancer-tissue-classification

Note: You can use google colab for the training of the 2 models.

Payment clause: Payment will be done when both models have been developed and trained and full comparative analysis reports are done.