Convolutional Neural Network Model for Segmentation and Classification of Clear Cell Renal Cell Carcinoma Based on Multiphase CT Images.
Vlad-Octavian BolocanMihaela SecareanuElena SavaCosmin MedarLoredana Cornelia Sabina ManolescuAlexandru-Ștefan Cătălin RașcuMaria Glencora CostacheGeorge Daniel RadavoiRobert-Andrei DobranViorel JingaPublished in: Journal of imaging (2023)
(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm's ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. (2) Methods: This study uses the European Deep-Health toolkit's Convolutional Neural Network with ECVL, (European Computer Vision Library), and EDDL, (European Distributed Deep Learning Library). Image segmentation utilized U-net architecture and classification with resnet101. The model's clinical efficiency was assessed utilizing kidney, tumor, Dice score, and renal cell carcinoma categorization quality. (3) Results: The raw dataset contains 457 healthy right kidneys, 456 healthy left kidneys, 76 pathological right kidneys, and 84 pathological left kidneys. Preparing raw data for analysis was crucial to algorithm implementation. Kidney segmentation performance was 0.84, and tumor segmentation mean Dice score was 0.675 for the suggested model. Renal cell carcinoma classification was 0.885 accurate. (4) Conclusion and key findings: The present study focused on analyzing data from both healthy patients and diseased renal patients, with a particular emphasis on data processing. The method achieved a kidney segmentation accuracy of 0.84 and mean Dice scores of 0.675 for tumor segmentation. The system performed well in classifying renal cell carcinoma, achieving an accuracy of 0.885, results which indicates that the technique has the potential to improve the diagnosis of kidney pathology.
Keyphrases
- deep learning
- renal cell carcinoma
- convolutional neural network
- computed tomography
- artificial intelligence
- machine learning
- big data
- dual energy
- electronic health record
- end stage renal disease
- image quality
- positron emission tomography
- contrast enhanced
- high resolution
- primary care
- gene expression
- public health
- prognostic factors
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- social media
- magnetic resonance
- risk assessment
- mass spectrometry
- data analysis
- photodynamic therapy
- patient reported outcomes
- optical coherence tomography