ArticleCancers2021
Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Pathomics-based machine learning models for predicting METTL5 expression and prognosis in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- The Prognostic and Biological Value of PGF-Based H&E Pathomics in Hepatocellular Carcinoma.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- ALOX5 Expression and Pathomics Features Reveal New Insights Into Lung Adenocarcinoma Prognosis: Model Construction and Functional Validation.Human mutation · 2026Article
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma.Journal of imaging informatics in medicine · 2025Article
- Pathomics-based machine learning model predicts interferon-gamma expression in head and neck squamous cell carcinoma patients.Frontiers in immunology · 2025Article
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- Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2.PeerJ. Computer science · 2025Article
- Predicting prognosis in lung adenocarcinoma by predicting TIGIT expression: a pathomics model.Journal of thoracic disease · 2024Article
- Pathomic and bioinformatics analysis of clinical-pathological and genomic factors for pancreatic cancer prognosis.Scientific reports · 2024Article
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- Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism.Heliyon · 2024Article
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- Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer.Clinical and experimental otorhinolaryngology · 2024Article
- Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques.PeerJ. Computer science · 2024Article
- Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.Biomedical engineering online · 2023Article
- A quantitative evaluation method utilizing the homology concept to assess the state of chromatin within the nucleus of lung cancer.Scientific reports · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
The purpose of this study was to develop a computer-aided diagnosis (CAD) system for automatic classification of histopathological images of lung tissues. Two datasets (private and public datasets) were obtained and used for developing and validating CAD. The private dataset consists of 94 histopathological images that were obtained for the following five categories: normal, emphysema, atypical adenomatous hyperplasia, lepidic pattern of adenocarcinoma, and invasive adenocarcinoma. The public dataset consists of 15,000 histopathological images that were obtained for the following three categories: lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. These images were automatically classified using machine learning and two types of image feature extraction: conventional texture analysis (TA) and homology-based image processing (HI). Multiscale analysis was used in the image feature extraction, after which automatic classification was performed using the image features and eight machine learning algorithms. The multicategory accuracy of our CAD system was evaluated in the two datasets. In both the public and private datasets, the CAD system with HI was better than that with TA. It was possible to build an accurate CAD system for lung tissues. HI was more useful for the CAD systems than TA.
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