Skin-Lesion-Classification

Goal

In this task, we complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, we distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, we distinguish between (a) seborrheic keratosis and (b) nevus and melanoma.

Definitions:

Melanoma – malignant skin tumor, derived from melanocytes (melanocytic) Nevus – benign skin tumor, derived from melanocytes (melanocytic) Seborrheic keratosis – benign skin tumor, derived from keratinocytes (non-melanocytic)

Data:

Lesion classification data includes the original image, paired with a gold standard (definitive) diagnosis, referred to as "Ground Truth".

Training Image Data

2000 images are provided as training data, including 374 "melanoma", 254 "seborrheic keratosis", and the remainder as benign nevi (1372). The training data is provided as a ZIP file, containing dermoscopic lesion images in JPEG format and a CSV file with some clinical metadata for each image.

All images are named using the scheme ISIC_<image_id>.jpg, where <image_id> is a 7-digit unique identifier. EXIF tags in the images have been removed; any remaining EXIF tags should not be relied upon to provide accurate metadata.

The CSV file contains three columns:

image_id, identifying the image that the row corresponds to age_approximate, containing the age of the lesion patient, rounded to 5 year intervals, or "unknown" sex, containing the sex of the lesion patient, or "unknown" Ground Truth Data

The Training Ground Truth file is a single CSV (comma-separated value) file, containing 3 columns:

The first column of each row contains a string of the form ISIC_<image_id>, where <image_id> matches the corresponding Training Data image. The second column of each row pertains to the first binary classification task (melanoma vs. nevus and seborrheic keratosis) and contains the value 0 or 1. The number 1 = lesion is melanoma The number 0 = lesion is nevus or seborrheic keratosis The third column of each row pertains to the second classification task (seborrheic keratosis vs. melanoma and nevus) and contains the value 0 or 1. The number 1 = lesion is seborrheic keratosis The number 0 = lesion is melanoma or nevus Malignancy diagnosis data were obtained from expert consensus and pathology report information.

Project Structure

For the report see Report.pdf Contains detailed explanation, results and conclusion.

Code

Contains the code for the project

• TFRecordCreator

  Contains code for converting raw data to TFrecord for better performance using tf.data API

  Change the addresses in TFrecord_writer.py according to your system. Run TFrecord_writer.py to convert image and label dataset to a .tfrecords file Run TFrecord_writer_augment.py to augment and convert the images and label dataset to a .tfrecords file Run TFrecord_writer_augment_addPositives.py to augment and convert just the positive images & labels to a .tfrecords file. This is done to balance the unbalanced dataset by increasing the count of the underrepresented category. Run Sanity_check.py to check the images and labels from .tfrecords file

• Train

  Contains code for training the data, resnet model and to plot the model metrics.

  • resnet.py: Contains the resnet model with 18, 34, 50, 101 and 152 layers

  • Train_resnet.py: Contains the training code for resnet without fine-tuning. Run this to train your model.

  • Train_Fine_resnet.py: Contains the training code for resnet with pre-trained weights with fine-tuning. Run this to train your model.

  • plot.py: Plots the graph for the model performance metrics from trainHistoryDict

  • Test.py: Tests the model using the saved weights from Train_resnet.py. This is for the model without fine-tuning

  • Test_fine_resnet.py: Tests the model using the saved weights from Train_Fine_resnet.py. This is for the model with fine-tuning.

  • train_resnet.py: Train custom ResNet from scratch.

This project is conducted by Mudit Chaudhary ([email protected]) and Jiyeon Byun ([email protected]) under the supervision of Prof. K.S. Leung and Mr. FU Xinyu.