ArticleSensors (Basel, Switzerland)2021
A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 34 citations in OpenAlex.
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
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- MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.PloS one · 2024Article
- Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Data-Driven Personalized Care in Lung Cancer: Scoping Review and Clinical Recommendations on Performance Status and Activity Level of Patients With Lung Cancer Using Wearable Devices.JCO clinical cancer informatics · 2023Article
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning.Healthcare (Basel, Switzerland) · 2022Article
- Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement.Healthcare (Basel, Switzerland) · 2022Article
- Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.Computational and mathematical methods in medicine · 2022Article
- Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation.Computational intelligence and neuroscience · 2022Article
- BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation.Computational intelligence and neuroscience · 2022Article
- A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries.Computational intelligence and neuroscience · 2022Article
- Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net.Computational intelligence and neuroscience · 2022Article
- Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.