/Skin-Cancer-Android-App

This app contains and skin cancer android app whose model is created using transfer learning with inception_v3

Primary LanguageJupyter Notebook

Skin Cancer Detection Android App(Image Processing)

Here are the Codes, Datasets and the Android App required to create the Skin Cancer Detection App.

Data

The dataset is orignally available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T . follow the steps given on https://github.com/syed-hamza/Skin-Cancer-Android-App/blob/master/Data.md for dataset prepartion.

Structure

The data should be arranged in the following struture.
https://github.com/syed-hamza/Skin-Cancer-Android-App/blob/master/input.md

Code

  1. Inception-V3 - https://github.com/syed-hamza/Skin-Cancer-Android-App/blob/master/src/SkinCancer_inceptionModel.ipynb
  2. nasnet - https://github.com/syed-hamza/Skin-Cancer-Android-App/blob/master/src/SkinCancer_nasnetmobile.ipynb

Inference

The given code uses inception_v3 at https://keras.io/applications/#inceptionv3.Inception model gave me a slightly better result than nasnetmobile and training the model was comparitively quick.

Inception(Validation_Accuracy):

inception

validation accuracy:85.76%

NasNetMobile(Validation_Accuracy):

nasnet

validation accuracy:84.72%

From this we can conclude that eventhough nasnet fluctuates less than inception inception provides a better accuracy,hence is better suited for the app.

Regards

I am a student in highschool and this is my first project using Deep Learning. The following project took me several weekends along 2 months. I hope it is helpful to everyone reading this. All suggetions are welcome.