Multi classfication of medical images

+Newbie alert

I followed the steps mentioned in lesson 3 on fundus (medical) image dataset (400 images) . I tried two different image augmentations
get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.) // as in lesson 3

tfms = get_transforms(max_rotate=30, max_zoom=1.3, max_lighting=0.4, max_warp=0.0,
p_affine=1., p_lighting=1.)

Even after multiple levels , lr find -> train-> unfreeze-> lr find -> train , followed by two sets of training on higher resolution images , I am not able to cross 83-84 % accuracy .
I tried on both densenet101 and resnet101 pretrained models . Didnt get good results in resnet50

As I understand , dataset is much from ordinary datasets . My gut feeling says I need to train many cycles to get closer to 92-93 %
Any other suggestions on how to improve accuracy ?

Thanks in advance

What type of medical image (microscopic/DICOM/WSI)? Please share your kernel for others to look and comment at.

Images look something like , its not dicom , its a normal tiff image.

You can look at the approaches here

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Can you share your notebook.
It would be helpful.