/HAM10000_ConvNet

Convolutional neural network capable of identifying skin lesions (based on the skin lesion image data set HAM10000).

Primary LanguageJupyter Notebook

Convolutional neural network based on the skin lesion image data set HAM10000.

Created as part of Project 2 for module COS624 (Cognitive Systems M.Sc.).

Results

I trained 5 versions of the ConvNet, using 5 different data sets:

  1. Data set with 8x8 pixel images (as provided in the HAM data set).
  2. Data set with 28x28 images (as provided in the HAM data set).
  3. Data set with 28x28 images, same as above, but with augmented lesion types to 2000 images per category.
  4. Data set with 42x42 images, that I created, with augmented lesion types to 2000 per category.
  5. Data set with 64x64 images, that I created, with augmented lesion types to 2000 per category.

All networks were trained for 30 epochs. The table below summarises the results:

Test accuracy Test loss Training time Epochs
8x8 (as provided) 0.710934 0.86682 0:00:49 30
28x28 (as provided) 0.729905 0.785197 0:06:07 30
28x28 (augmented) 0.62069 0.999897 0:11:25 30
42x42 (augmented) 0.659984 0.944596 0:25:51 30
64x64 (augmented) 0.652767 1.10109 0:54:19 30

Additionally, the figure below shows the training histories, outcomes and confusion matrices for all networks:

Part 2