{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:RH4EVQDTGBZG6ATLAPL72A4QZM","short_pith_number":"pith:RH4EVQDT","schema_version":"1.0","canonical_sha256":"89f84ac07330726f026b03d7fd0390cb0cbf07f77a9533ae6c92302aae6e57d0","source":{"kind":"arxiv","id":"2010.01362","version":2},"attestation_state":"computed","paper":{"title":"COVID-19 Classification of X-ray Images Using Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Ahuva Grubstein, Amiel A Dror, Ayelet Blass, Chedva S Weiss, Daniel Yaron, Daphna Keidar, Dimitri Lumelsky, Dror Suhami, Elisha Goldstein, Eyal Sela, Gil N Bachar, Israel Aharony, Leonid Charbinsky, Liza Lifshitz, Majd Hajouj, Matti Mizrachi, Naama R Bogot, Nethanel Eizenbach, Nogah Shabshin, Ofer Benjaminov, Philip Levin, Shlomit Tamir, Yael Rapson, Yair Shachar, Yishai M Elyada, Yonina C Eldar, Ziv Neeman","submitted_at":"2020-10-03T13:57:08Z","abstract_excerpt":"In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50)"},"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":"2010.01362","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-10-03T13:57:08Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"3a7a738db0d2ebb0900060eb9764941626963081cdcbc96b206fd563e7a0acb4","abstract_canon_sha256":"d207c6ba5d83ad7623152f2499e1216648900f7cc92c5b6d5f04fbf16895d980"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:41:10.242773Z","signature_b64":"rjJPF+3r/0ibr3e3GWdgXO2fKlAJaN2mP2ShH286BRR9e/vQ31+qtYvlYEUQLzE/2tjiV4oNldtDyJRlmdE0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89f84ac07330726f026b03d7fd0390cb0cbf07f77a9533ae6c92302aae6e57d0","last_reissued_at":"2026-07-05T01:41:10.242287Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:41:10.242287Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COVID-19 Classification of X-ray Images Using Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Ahuva Grubstein, Amiel A Dror, Ayelet Blass, Chedva S Weiss, Daniel Yaron, Daphna Keidar, Dimitri Lumelsky, Dror Suhami, Elisha Goldstein, Eyal Sela, Gil N Bachar, Israel Aharony, Leonid Charbinsky, Liza Lifshitz, Majd Hajouj, Matti Mizrachi, Naama R Bogot, Nethanel Eizenbach, Nogah Shabshin, Ofer Benjaminov, Philip Levin, Shlomit Tamir, Yael Rapson, Yair Shachar, Yishai M Elyada, Yonina C Eldar, Ziv Neeman","submitted_at":"2020-10-03T13:57:08Z","abstract_excerpt":"In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.01362","kind":"arxiv","version":2},"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/2010.01362/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":"2010.01362","created_at":"2026-07-05T01:41:10.242356+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.01362v2","created_at":"2026-07-05T01:41:10.242356+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.01362","created_at":"2026-07-05T01:41:10.242356+00:00"},{"alias_kind":"pith_short_12","alias_value":"RH4EVQDTGBZG","created_at":"2026-07-05T01:41:10.242356+00:00"},{"alias_kind":"pith_short_16","alias_value":"RH4EVQDTGBZG6ATL","created_at":"2026-07-05T01:41:10.242356+00:00"},{"alias_kind":"pith_short_8","alias_value":"RH4EVQDT","created_at":"2026-07-05T01:41:10.242356+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/RH4EVQDTGBZG6ATLAPL72A4QZM","json":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM.json","graph_json":"https://pith.science/api/pith-number/RH4EVQDTGBZG6ATLAPL72A4QZM/graph.json","events_json":"https://pith.science/api/pith-number/RH4EVQDTGBZG6ATLAPL72A4QZM/events.json","paper":"https://pith.science/paper/RH4EVQDT"},"agent_actions":{"view_html":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM","download_json":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM.json","view_paper":"https://pith.science/paper/RH4EVQDT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.01362&json=true","fetch_graph":"https://pith.science/api/pith-number/RH4EVQDTGBZG6ATLAPL72A4QZM/graph.json","fetch_events":"https://pith.science/api/pith-number/RH4EVQDTGBZG6ATLAPL72A4QZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM/action/storage_attestation","attest_author":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM/action/author_attestation","sign_citation":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM/action/citation_signature","submit_replication":"https://pith.science/pith/RH4EVQDTGBZG6ATLAPL72A4QZM/action/replication_record"}},"created_at":"2026-07-05T01:41:10.242356+00:00","updated_at":"2026-07-05T01:41:10.242356+00:00"}