This project aims to predict the probability of leprosy using skin lesion images and clinical data (as compared to the diagnosis of dermatologists). This model is provided for research and development use only. The model is not intended for use in clinical decision-making or for any other clinical use and the performance of model for clinical use has not been established.

Required R packages in r_requirement.txt Required python packages in python_requirement.txt

To replicate cross-validation experiments for Model 1 by resnet50 using close-up images using python:

from run_model import * for test_id in range(5): run_model( id=0, # cuda id save_dir=ADDRESS_TO_SAVE_EXP_RESULT, experiment_name = 'CV_MODEL1_BY_RESNET50_USING_CLOSEUP', batch_size=32, tuning='tune_all', num_epoch=200, model_name='resnet50', scale=.6, dataset_name='images', type_spec='closeup', change_aspect_ratio=None, num_fold=6, test_foldid=test_id)

Build Model 2 using R

source("clean_metadata.R") source("run_lr.R") source("run_rf.R") source("run_xgboost.R") dt<-clean_img_metadata(read.csv(ADDRESS_TO_PATIENT_FORM,header=TRUE), read.csv(ADDRESS_TO_LESION_FORM,header=TRUE)) img_metadata<-dt[['img']] patient_info<-dt[['patient']] drop<-c("areanearoflesion", "Contralateralarea") # these are highly correlated with the other three temperature features df<-img_metadata[,-which(names(img_metadata)%in% drop)] train_df=df[which(df$StudyID %in% PATIENT_TRAIN),] test_df=df[which(df$StudyID %in% PATIENT_TEST),] df_preprocess<-preprocess(train_df,test_df,cutoff=.8,standardize=TRUE,impute=TRUE) lr_result<-run_logit(df_preprocess$train,df_preprocess$test,repeated_cv = 5) xgb_result<-run_xgb(df_preprocess$train,df_preprocess$test,repeated_cv=3) rf_result<-rf_pi(df_preprocess$train,df_preprocess$test)

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