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neheller / Kits19The official repository of the 2019 Kidney and Kidney Tumor Segmentation Challenge
MrGiovanni / DiffTumor[CVPR 2024] Generalizable Tumor Synthesis - Realistic Synthetic Tumors in Liver, Pancreas, and Kidney
neheller / Kits21The official repository of the 2021 Kidney and Kidney Tumor Segmentation Challenge
neheller / Kits23The official repository of the 2023 Kidney Tumor Segmentation Challenge (KiTS23)
krishnaik06 / Kidney Disease Classification Deep Learning ProjectNo description available
junqiangchen / KiTS19 ChallegeKiTS19——2019 Kidney Tumor Segmentation Challenge
kanchitank / Medibuddy Smart Disease PredictorMultiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.
nitsaick / Kits19 ChallengeKidney Tumor Segmentation Challenge 2019
rsingla92 / KidneyUSTowards ubiquitous ultrasound.
siv2r / Kidney ExchangeA platform to facilitate automized inter-hospital kidney transplants.
MrGiovanni / Pixel2Cancer[MICCAI 2024] Cellular Automata for Tumor Development - Realistic Synthetic Tumors in Liver, Pancreas, and Kidney
junqiangchen / AttentionGatedVNet3DAttention Gated VNet3D Model for KiTS19——2019 Kidney Tumor Segmentation Challenge
muellerdo / Kits19.MIScnnKidney Tumor Segmentation Challenge 2019: MIScnn - 3D Residual U-Net
liuy14 / Kidney Disease DetectionThis is a classification problem to predict kidney disease
abhayshah0305 / RiskAssessA web application that predicts if a patient has cancer, diabetes, heart disease, kidney disease, and liver disease based on machine learning models.
arnabsinha99 / Kidney Stone Detection IPA project to detect Kidney stone in the Ultrasound and/or CT scan images using Image processing and Machine Learning.
venkata-sreeram / Chronic Kidney Disease PredictionNo description available
KaziAmitHasan / Prediction Of Clinical Risk Factors Of Diabetes Using ML Resolving Class ImbalanceBeing the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.
constantAmateur / ScKidneyTumorsCode for "Single cell transcriptomes from human kidneys reveal the cellular identity of renal tumours"
wenshuaizhao / MSSU NetThis code is for the paper "multi-scale supervised 3D U-Net for kidneys and kidney tumor segmentation".
