ArticleFrontiers in oncology2023
A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 37 citations in OpenAlex.
- Multiclass lung cancer detection using a hybrid capsule inspired deep neural network.Scientific reports · 2026Article
- KDH-Net: Explainable Medical AI for Multiclass Kidney Disease Characterization from CT Images.Journal of clinical medicine · 2026Article
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying Kidney Stone Risk Factors Through Patient Experiences With a Large Language Model: Text Analysis and Empirical Study.Journal of medical Internet research · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Driven early detection of chronic kidney cancer disease based on machine learning technique.PloS one · 2025Article
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.Scientific reports · 2024Article
- Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.Journal of imaging · 2024Article
- A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors.Frontiers in oncology · 2024Article
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Authors and funding
9 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.
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