{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RP2TMHGF732SP742BF4DEIKOA5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"61e7c1c6cc52868239d67f4bba390107c82170b7eae5a8b392ddc0c92ef2af3b","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-02-15T18:23:34Z","title_canon_sha256":"d9e58d5a3fd208e3ef3095740318b04fc7c18fd1047640a2ab8b77e84c148b25"},"schema_version":"1.0","source":{"id":"2102.07726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.07726","created_at":"2026-07-05T02:15:19Z"},{"alias_kind":"arxiv_version","alias_value":"2102.07726v1","created_at":"2026-07-05T02:15:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.07726","created_at":"2026-07-05T02:15:19Z"},{"alias_kind":"pith_short_12","alias_value":"RP2TMHGF732S","created_at":"2026-07-05T02:15:19Z"},{"alias_kind":"pith_short_16","alias_value":"RP2TMHGF732SP742","created_at":"2026-07-05T02:15:19Z"},{"alias_kind":"pith_short_8","alias_value":"RP2TMHGF","created_at":"2026-07-05T02:15:19Z"}],"graph_snapshots":[{"event_id":"sha256:65356e006615e91a3466275956df1f399a40179b5e86f0ec7d3c21bde5054a2d","target":"graph","created_at":"2026-07-05T02:15:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2102.07726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Since the breakout of coronavirus disease (COVID-19), the computer-aided diagnosis has become a necessity to prevent the spread of the virus. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography (CT) images Furthermore, the system classifies the severity of COVID-19 as mild, moderate, severe, or critical based on the percentage of infected lungs. An extensive set of experiments were performed using state-of-the-","authors_text":"Amith Khandakar, Anas Tahir, Farayi Musharavati, Muhammad E. H. Chowdhury, Nabil Ibtehaz, Sakib Mahmud, Serkan Kiranyaz, Somaya Al-Madeed, Tawsifur Rahman, Yazan Qiblawey","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-02-15T18:23:34Z","title":"Detection and severity classification of COVID-19 in CT images using deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.07726","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:552fe8079e93cf86e79115633822269153bbbe0453f23055ee1e18ea5b2536b8","target":"record","created_at":"2026-07-05T02:15:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"61e7c1c6cc52868239d67f4bba390107c82170b7eae5a8b392ddc0c92ef2af3b","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-02-15T18:23:34Z","title_canon_sha256":"d9e58d5a3fd208e3ef3095740318b04fc7c18fd1047640a2ab8b77e84c148b25"},"schema_version":"1.0","source":{"id":"2102.07726","kind":"arxiv","version":1}},"canonical_sha256":"8bf5361cc5fef527ff9a097832214e074d0015709ab83b9bdb0f569484092191","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8bf5361cc5fef527ff9a097832214e074d0015709ab83b9bdb0f569484092191","first_computed_at":"2026-07-05T02:15:19.422232Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:15:19.422232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UtSRoFr4aM8lHAENE89k/taAXk4qq9AuBBxjJUcMb4O8qhEYdvlRnMr3zVZy4zzMtQQ1FxDdYn25U3io0F+CAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:15:19.422687Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.07726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:552fe8079e93cf86e79115633822269153bbbe0453f23055ee1e18ea5b2536b8","sha256:65356e006615e91a3466275956df1f399a40179b5e86f0ec7d3c21bde5054a2d"],"state_sha256":"b4c198fcd87416e9ce6b046fdba8b06e479880b364a1fefa9f1b1a566f02a286"}