{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6WI6KHNSRQQDSASY2DIZ62WHRM","short_pith_number":"pith:6WI6KHNS","schema_version":"1.0","canonical_sha256":"f591e51db28c20390258d0d19f6ac78b2bd87e8d3fbf03ced2bc8a84811d31b5","source":{"kind":"arxiv","id":"2102.06535","version":1},"attestation_state":"computed","paper":{"title":"Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Essam H. Houssein, Mohamed Elhoseny, Waleed M. Mohamed, Zainab Abohashima","submitted_at":"2021-02-08T18:22:53Z","abstract_excerpt":"Despite the great efforts to find an effective way for COVID-19 prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via Chest X-Ray (CXR) images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN) model used the random quantum circuits (RQCs) as a base to detect COVID-19 patients with CXR images. A collection of 6952 CXR images, including 1161 COVID-19, 1575 normal, and 5216 pneumonia images, were used"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2102.06535","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-08T18:22:53Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"cf1977c61201471dd55aa628dec31178f0e81509149a61caa53ebdaa0fb3ef85","abstract_canon_sha256":"1f5caeba63a70907b357cbdcdecce241b21a17a755e6b81781151fd9dfe21b48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:03:44.705205Z","signature_b64":"2DGKaqoKg9Aj8Rr3HMW0S4KPY7pbIu0mZB+s7G+POtNN2nAZoODRyjbor7zRZku0Ik5+4RiU+n3mZW2sKN9FDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f591e51db28c20390258d0d19f6ac78b2bd87e8d3fbf03ced2bc8a84811d31b5","last_reissued_at":"2026-07-05T04:03:44.704705Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:03:44.704705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Essam H. Houssein, Mohamed Elhoseny, Waleed M. Mohamed, Zainab Abohashima","submitted_at":"2021-02-08T18:22:53Z","abstract_excerpt":"Despite the great efforts to find an effective way for COVID-19 prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via Chest X-Ray (CXR) images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN) model used the random quantum circuits (RQCs) as a base to detect COVID-19 patients with CXR images. A collection of 6952 CXR images, including 1161 COVID-19, 1575 normal, and 5216 pneumonia images, were used"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.06535","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2102.06535/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2102.06535","created_at":"2026-07-05T04:03:44.704762+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.06535v1","created_at":"2026-07-05T04:03:44.704762+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.06535","created_at":"2026-07-05T04:03:44.704762+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WI6KHNSRQQD","created_at":"2026-07-05T04:03:44.704762+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WI6KHNSRQQDSASY","created_at":"2026-07-05T04:03:44.704762+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WI6KHNS","created_at":"2026-07-05T04:03:44.704762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM","json":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM.json","graph_json":"https://pith.science/api/pith-number/6WI6KHNSRQQDSASY2DIZ62WHRM/graph.json","events_json":"https://pith.science/api/pith-number/6WI6KHNSRQQDSASY2DIZ62WHRM/events.json","paper":"https://pith.science/paper/6WI6KHNS"},"agent_actions":{"view_html":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM","download_json":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM.json","view_paper":"https://pith.science/paper/6WI6KHNS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.06535&json=true","fetch_graph":"https://pith.science/api/pith-number/6WI6KHNSRQQDSASY2DIZ62WHRM/graph.json","fetch_events":"https://pith.science/api/pith-number/6WI6KHNSRQQDSASY2DIZ62WHRM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM/action/storage_attestation","attest_author":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM/action/author_attestation","sign_citation":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM/action/citation_signature","submit_replication":"https://pith.science/pith/6WI6KHNSRQQDSASY2DIZ62WHRM/action/replication_record"}},"created_at":"2026-07-05T04:03:44.704762+00:00","updated_at":"2026-07-05T04:03:44.704762+00:00"}