Detection of skin lesions (among 7 classes) using the file https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T and using the pytorch resnet model. The success rate for the specific test file (unseen data) that comes with the download file is 81.13%.
According to the specifications of the download file, the 7 types of injuries to be detected are:
akiec : Actinic keratoses and intraepithelial carcinoma / Bowen’s disease
bkl : benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses
bcc: basal cell carcinoma
df: dermatofibroma
mel: melanoma
nv: melanocytic nevi
vasc: vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage
All packages, if any are missing, can be installed with a simple pip in case the programs indicate their absence in the environment.
Download all the files that accompany this project in a single folder.
By downloading the file from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T in the directory where the project is located, a file called dataverse_files.zip is obtained, which once decompressed as dataverse_files contains, among others, the files HAM10000_images_part1.zip and HAM10000_images_part2.zip, which once unzipped must be unified into a single HAM10000_images folder (through a simple copy and paste) in the same dataverse_files directory
Next, the structure necessary for the operation of resnet pytorch is created, consisting of a folder Dir_SkinCancer_Resnet_Pytorch from which a folder called train and another called valid hang, each with a subfolder for each of the 7 classes, by executing:
Create_DirSkinCancer_Resnet_Pytorch.py
This structure is then filled from the images contained in dataverse_files\HAM10000_images and following the order indicated in the file dataverse_files\HAM10000_metadata, by executing:
Fill_DirSkinCancer_Resnet_Pytorch.py
To avoid resnet errors if you find a valid folder in which one of its subfolders does not have images: unzip the attached valid.zip and copy the resulting valid folder (be careful there may be two nested valid ones, only consider the last one) over the folder Dir_SkinCancer_Resnet_Pytorch overwriting the old one, this way you ensure that all valid subfolders have at least some image.
The model is trained and obtained by executing:
Train224x224_SkinCancer_Resnet_Pytorch.py
The execution log is attached as a file LOG_TrainSkinCancer_10epoch.txt and as a result the model checkpoint_SkinCancer_10epoch.pth is obtained (it is not attached because its size exceeds the file size limit that can be uploaded to github)
Next, the model obtained is tested: checkpoint_SkinCancer_10epoch.pth with the data from the specific test file that is attached to the download as ISIC2018_Task3_Test_Images.zip (it must be unzipped)
Next, the structure necessary for the operation of resnet pytorch is created with the specific test file, consisting of a folder Dir_Test_SkinCancer_Resnet_Pytorch from which a folder called test hangs with subfolders for each of the 7 classes, by executing:
Create_Test_DirSkinCancer_Resnet_Pytorch.py
This structure is then filled from the images contained in dataverse_files\ISIC2018_Task3_Test_Images\ISIC2018_Task3_Test_Images and following the order indicated in the file dataverse_files\ISIC2018_Task3_Test_GroundTruth.csv, by executing:
Fill_Test_DirSkinCancer_Resnet_Pytorch.py
The program is then executed:
Guess_Test_224x224SkinCancer_Resnet_Pytorch.py
Who performs the test
The log of its execution is attached as LOG_Test_SkinCancer.txt
The screen indicates the successes and failures, giving a success rate of 81.13%
To obtain predictions on images for which the skin lesion classification is not known and which are assumed to be in a folder called Test within the project, the program will be executed:
Recognize_SkinCancer_Resnet_Pytorch.py
Through the console, the prediction is obtained for each image and also in the output file
ModelsResults.txt
The success rate may change if images of different quality than those used in the training process are tested. The program comes prepared to test 8 images (Test folder attached that must be unzipped) downloaded from https://www.skincancer.org/es/skin-cancer-information/skin-cancer-pictures/ and interpretation of the accuracy or approximation would require an expert. By modifying the path in line 90 of the program, you can test the set of images you want.
References:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
https://www.kaggle.com/code/hadeerismail/skin-cancer-prediction-cnn-acc-98
https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000
https://www.skincancer.org/es/skin-cancer-information/skin-cancer-pictures/