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dasoto / skincancer

Skin cancer detection project
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cnn-keras skin-cancer

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CNN to identify malign moles on skin

by David Soto - dasoto@gmail.com

@Galvanize Data Science Immersive Program

1. Project Summary and motivation

The purpose of this project is to create a tool that considering the image of a mole, can calculate the probability that a mole can be malign.

Skin cancer is a common disease that affect a big amount of peoples. Some facts about skin cancer:

2. Development process and Data

The idea of this project is to construct a CNN model that can predict the probability that a specific mole can be malign.

2.1 Data:

To train this model the data to use is a set of images from the International Skin Imaging Collaboration: Mellanoma Project ISIC https://isic-archive.com.

The specific datasets to use are:

As summary the total images to use are:

Benign Images Malignant Images
1208 849

Some sample images are shown below:

  1. Sample images of benign moles:

2.2 Preprocessing:

The following preprocessing tasks are developed for each image:

  1. Visual inspection to detect images with low quality or not representative
  2. Image resizing: Transform images to 128x128x3
  3. Crop images: Automatic or manual Crop
  4. Other to define later in order to improve model quality

2.3 CNN Model:

The idea is to develop a simple CNN model from scratch, and evaluate the performance to set a baseline. The following steps to improve the model are:

  1. Data augmentation: Rotations, noising, scaling to avoid overfitting
  2. Transferred Learning: Using a pre-trained network construct some additional layer at the end to fine tuning our model. (VGG-16, or other)
  3. Full training of VGG-16 + additional layer.

2.4 Model Evaluation:

To evaluate the different models we will use ROC Curves and AUC score. To choose the correct model we will evaluate the precision and accuracy to set the threshold level that represent a good tradeoff between TPR and FPR.

3. Results presentation

As mention before the idea is to generate a tool to predict the probability of a malign mole. To do it, I'm planning to provide the following resources:

1. Web App: The web app will have the possibility that a user upload a high quality image of an specific mole. The results will be a prediction about the probability that the given mole be malign in terms of percentage. The backend that contain the web app and model loaded will be located in Amazon Web Services.

2. Iphone App: Our CNN model will be loaded into the iPhone to make local predictions. Advantages: The image data don't need to be uploaded to any server, because the model predictions can be done through the pre-trained model loaded into the iPhone.

3. Android App: (Optional if time allow it)

4. Project Schedule

Activity Days Status Prog
1. Data Acquisition 1 Done ++++
2. Initial Preprocessing and visualizations 1 Done ++++
3. First Model Construction and tuning 2 Done ++++
4. Model Optimization I (Data augmentation) 1 Done ++++
5. Model Optimization II (Transferred learning) 2 Done ++++
6. Model Optimization III (Fine Tuning) 2 Done ++++
7. Web App Development + Backend Service  2 Done ++++
8. Ios App Development 2 Done ++++
9. Android App Development 2 Pending ----
10. Presentation preparation 1 Done ++++

5. Tools to Use

6. Final Results

First Model: CNN from scratch, no data augmentation

Simple Convolutional Neural Network with 3 layers. The results obtained until now can be shown on the ROC curve presented below:

Classification Report CNN From scratch, CV Folder.
class precision recall f1-score support
0.0 0.86 0.88 0.87 50
1.0 0.88 0.86 0.87 50
avg / total 0.87 0.87 0.87 100

Second Model: VGG16 + Dense Layer

Classification Report VGG16 + Dense Layer.
class precision recall f1-score support
0.0 0.87 0.92 0.89 50
1.0 0.91 0.86 0.89 50
avg / total 0.89 0.89 0.89 100
Classification Report VGG16 + Dense Layer.
class precision recall f1-score support
0.0 0.82 0.94 0.88 50
1.0 0.93 0.80 0.86 50
avg / total 0.88 0.87 0.87 100

Third Model: CNN + Data Augmentation

Classification Report CNN Scratch with Data Augmentation.
class precision recall f1-score support
0.0 0.81 0.96 0.88 50
1.0 0.95 0.78 0.86 50
avg / total 0.88 0.87 0.87 100

Fourth Model: VGG16 + Dense Layer + Data Augmentation

Classification Report VGG16 with Data Augmentation.
class precision recall f1-score support
0.0 0.88 0.88 0.88 50
1.0 0.88 0.88 0.88 50
avg / total 0.88 0.88 0.88 100

6.1 CNN Architecture:

All the layers have a Relu activation function, except the last one that is sigmoid, to obtain the probability of a Malignant mole.

6.2 iOS App

As part of this project I have developed an iOS app using the coreML libraries released by apple. The advantage to use this libraries is that the model and the image are stored locally on the phone, and internet connection is not needed. The keras model trained before is converted into coreML model and loaded into the phone to make the predictions. Below is a picture of the app and two examples of results.

6.3 webApp

In order to kae in consideration the user of different platforms, I also create a web App that can be accessed on: http://skinmolesrisk.ddns.net:7000 This app is responsive so can be used directly from any mobile phone or web browser.

7. Next Steps

8. Disclaimer

This tool has been designed only for educational purposes to demonstrate the use of Machine Learning tools in the medical field. This tool does not replace advice or evaluation by a medical professional. Nothing on this site should be construed as an attempt to offer a medical opinion or practice medicine.