{"doi":"10.1002/hsr2.71647","title":"A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X‐Ray Images: A Model Development and Validation Study","abstract":"<h4>Background</h4>Pathological diagnosis of chest X-ray images has always been a very challenging subject.<h4>Methods</h4>We propose a chest X-ray pathology detection network that fuses two depthwise separable convolutions (TDCheXNet) to detect pathology from chest X-ray images. We remove the transformation layer used by the original CheXNet and embed it in depthwise separable convolutions for down-sampling. In the first layer of convolution, to extract more pathological information, we use another depth-separable convolution to replace it and make full use of the negative X-axis features in the corresponding convolution layer.<h4>Results</h4>The ChestX-ray14 public chest X-ray dataset is used, which contains more than 100,000 frontal X-ray images covering 14 diseases. Using the area under the receiver operating characteristic curve (AUROC) as the evaluation index, calculate the average AUROC (AVG_AUROC). Experimental results show that TDCheXNet achieved 82.8% on the test set and the detection speed reached 178.794 ms, compared with the original model, AVG_AUROC is improved by 0.5%, and the single-image inference speed is increased by 14.946 ms.<h4>Conclusion</h4>We propose a new network for chest X-ray pathology detection, and experimental results show that it can achieve better performance on public datasets.","journal":"Health Science Reports","year":2025,"id":1441,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0363,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-12-25","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":17219,"name":"Guanghong Deng","orcid":"0009-0001-7293-3710","position":1,"is_corresponding":false},{"id":17220,"name":"Wenlong Jing","orcid":null,"position":2,"is_corresponding":false},{"id":17221,"name":"Yong Li","orcid":"0000-0001-5617-1659","position":3,"is_corresponding":false},{"id":2699,"name":"Li Li","orcid":"0000-0001-6746-4297","position":4,"is_corresponding":false},{"id":22216,"name":"Andrew W. McPherson","orcid":null,"position":5,"is_corresponding":false},{"id":17218,"name":"Na Zhang","orcid":null,"position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}