ArticleHealth information science and systems2019
Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).
Article in Health information science and systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation.Scientific reports · 2025Article
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- Research in the application of artificial intelligence to lung cancer diagnosis.Frontiers in medicine · 2024Review
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things.Bioengineering (Basel, Switzerland) · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model.Multimedia tools and applications · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
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- Gated Graph Attention Network for Cancer Prediction.Sensors (Basel, Switzerland) · 2021Article
- A decision support system for mammography reports interpretation.Health information science and systems · 2020Article
- Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network.Journal of digital imaging · 2020Article
- Clinicopathological characteristics and prediction of cancer-specific survival in large cell lung cancer: a population-based study.Journal of thoracic disease · 2020Article
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2 authors.
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Abstract
purposeA large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the traditional artificial neural networks (ANNs) as it uses deeper layers of convolutional neural network (CNN). The objective of the present study is to propose a simple yet fast CNN model combined with recurrent neural network (RNN) for automated AJCC staging of NSCLC and to compare the outcome with a few standard machine learning algorithms along with a few similar studies.
methodsThe NSCLC radiogenomics collection from the cancer imaging archive (TCIA) dataset was considered for the study. The tumor images were refined and filtered by resizing, enhancing, de-noising, etc. The initial image processing phase was followed by texture based image segmentation. The segmented images were fed into a hybrid feature detection and extraction model which was comprised of two sequential phases: maximally stable extremal regions (MSER) and the speeded up robust features (SURF). After a prolonged experiment, the desired CNN-RNN model was derived and the extracted features were fed into the model.
resultsThe proposed CNN-RNN model almost outperformed the other machine learning algorithms under consideration. The accuracy remained steadily higher than the other contemporary studies.
conclusionThe proposed CNN-RNN model performed commendably during the study. Further studies may be carried out to refine the model and develop an improved auxiliary decision support system for oncologists and radiologists.
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