ArticleScientific reports2023
A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Antidiabetic and antioxidant effects of Armillaria gallica and Armillaria cepistipes extracts and their silver nanoparticles against streptozotocin induced diabetes in mice.Scientific reports · 2026Article
- A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.Scientific reports · 2026Article
- A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.Scientific reports · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- A two-stage deep learning framework for kidney disease detection using modified specular-free imaging and EfficientNetB2.Scientific reports · 2026Article
- Artificial intelligence with deep convolutional neural network-based clinical decision-making on kidney Oncology using multimodal imaging.Scientific reports · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Automated Detection and Grading of Renal Cell Carcinoma in Histopathological Images via Efficient Attention Transformer Network.Medical sciences (Basel, Switzerland) · 2025Article
- MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.Scientific reports · 2025Article
- Computer vision assisted deep transfer learning model for accurate grading of renal cell carcinoma from kidney histopathology images.Scientific reports · 2025Article
- A multi-phase framework for enhancing diagnostic accuracy and transparency in renal cell carcinoma grading using YOLOv8 and GradCAM.Scientific reports · 2025Article
- Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images.Scientific reports · 2025Article
- Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images.Scientific reports · 2025Article
- Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.Scientific reports · 2025Article
- Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning algorithm for pathological grading of renal cell carcinoma based on multi-phase enhanced CT.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.Scientific reports · 2025Article
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5 authors.
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Abstract
Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80-85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity.
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