Skin Cancer Recognition

This project aims to develop a deep learning model to recognize skin cancer from images of skin lesions. Using the HAM10000 dataset, which consists of 10,000 dermatoscopic images of skin lesions, we train a Convolutional Neural Network (CNN) and several classifiers to classify different types of skin lesions and potentially identify skin cancer. Table of Contents

Introduction
Dataset
Model Architecture
Installation
Usage
Results
Contributing
License

Introduction

Skin cancer is one of the most common forms of cancer, and early detection is crucial for effective treatment. The goal of this project is to leverage machine learning, specifically a Convolutional Neural Network (CNN), to aid in the early detection and classification of skin cancer from images of skin lesions. Dataset

The HAM10000 ("Human Against Machine with 10000 training images") dataset is used in this project. It contains 10,000 dermatoscopic images of skin lesions, which are categorized into the following classes:

Melanocytic nevi
Melanoma
Benign keratosis-like lesions
Basal cell carcinoma
Actinic keratoses
Vascular lesions
Dermatofibroma

Downloading the Dataset

You can download the HAM10000 dataset from Kaggle.

Project Architecture

In our projects we have used two strategies to classify the lesions, one being feature extraction and then using various classsifers and the other on is creating and training a Convolutional Neural Network (CNN) based on the images and their metadata. Here is a high level diagram of our project:

Image preprocessing and data augmetation for the CNN

For our CNN we have resized all the images to 28x28, and augmented them by flipping, rotating and zooming. We have also sanitized the metadata and applied means to all null values.

CNN architecture

Layer (type)	Output Shape	Param #	Connected to
keras_tensor_351CL (InputLayer)	(None, 28, 28, 3)	0	-
conv2d_24 (Conv2D)	(None, 28, 28, 32)	896	keras_tensor_351CL
batch_normalization_19	(None, 28, 28, 32)	128	conv2d_24
re_lu_40 (ReLU)	(None, 28, 28, 32)	0	batch_normalization_19
conv2d_25 (Conv2D)	(None, 28, 28, 32)	9,248	re_lu_40
batch_normalization_20	(None, 28, 28, 32)	128	conv2d_25
re_lu_41 (ReLU)	(None, 28, 28, 32)	0	batch_normalization_20
conv2d_26 (Conv2D)	(None, 28, 28, 32)	9,248	re_lu_41
batch_normalization_21	(None, 28, 28, 32)	128	conv2d_26
re_lu_42 (ReLU)	(None, 28, 28, 32)	0	batch_normalization_21
max_pooling2d_11 (MaxPooling2D)	(None, 14, 14, 32)	0	re_lu_42
conv2d_27 (Conv2D)	(None, 14, 14, 64)	18,496	max_pooling2d_11
batch_normalization_22	(None, 14, 14, 64)	256	conv2d_27
re_lu_43 (ReLU)	(None, 14, 14, 64)	0	batch_normalization_22
conv2d_28 (Conv2D)	(None, 14, 14, 64)	36,928	re_lu_43
batch_normalization_23	(None, 14, 14, 64)	256	conv2d_28
keras_tensor_350CL (InputLayer)	(None, 23)	0	-
re_lu_44 (ReLU)	(None, 14, 14, 64)	0	batch_normalization_23
dense_21 (Dense)	(None, 128)	3,072	keras_tensor_350CL
conv2d_29 (Conv2D)	(None, 14, 14, 64)	36,928	re_lu_44
batch_normalization_24	(None, 128)	512	dense_21
batch_normalization_25	(None, 14, 14, 64)	256	conv2d_29
re_lu_46 (ReLU)	(None, 128)	0	batch_normalization_24
re_lu_45 (ReLU)	(None, 14, 14, 64)	0	batch_normalization_25
dense_22 (Dense)	(None, 128)	16,512	re_lu_46
max_pooling2d_12 (MaxPooling2D)	(None, 7, 7, 64)	0	re_lu_45
batch_normalization_26	(None, 128)	512	dense_22
flatten_5 (Flatten)	(None, 3136)	0	max_pooling2d_12
re_lu_47 (ReLU)	(None, 128)	0	batch_normalization_26
concatenate_5 (Concatenate)	(None, 3264)	0	flatten_5, re_lu_47
dense_23 (Dense)	(None, 1024)	3,343,360	concatenate_5
batch_normalization_27	(None, 1024)	4,096	dense_23
re_lu_48 (ReLU)	(None, 1024)	0	batch_normalization_27
dense_24 (Dense)	(None, 7)	7,175	re_lu_48

Classifiers

Parallel to the implementation of our CNN, we have decided it is a good idea to train some classifiers on the same dataset. We will use the following classifiers:

• Logistic Regression
• Random Forest
• SVM
• KNN
• AdaBoost
• Ensemble of the above classifiers

The classifiers where trained on features extracted from the images, a method from this paper.

Image preprocessing and augmentation for feature extraction

For feature extraction we have decided to balance the dataset by choosing a 100 images from each class, we then have augmented them by zooming, flipping and rotating.

Installation

To run this project, you need to have Python installed along with several libraries. You can install the required libraries using the following command:

pip install -r requirements.txt

Usage

Clone the repository:

git clone https://github.com/dawba/skin-cancer-recognition cd skin-cancer-recognition

Download and prepare the dataset:

Download the dataset from Kaggle

Place the dataset in the 'data' directory

mkdir data mv path_to_downloaded_dataset data/

Open the notebooks

Now you can open and run any of the included notebooks using your prefered Jupyter reader.

Results

After training all our classifiers and the CNN we have ended up with the CNN having a 0.81 accuracy score and the best classifier (Random Forest) having a 0.871 accuracy score. All other commonly used metrics can be found in the assorted notebooks.