{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YKFLVDFBKBZN7S2WATFNQ3TCXH","short_pith_number":"pith:YKFLVDFB","schema_version":"1.0","canonical_sha256":"c28aba8ca15072dfcb5604cad86e62b9f590578cc2436eb8366e40b448da7384","source":{"kind":"arxiv","id":"2306.14169","version":1},"attestation_state":"computed","paper":{"title":"A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anzirun Nahar Asma, Joydip Paul, Md. Tazuddin Ahmed, Nawsabah Noor, Shams Nafisa Ali, S. M. Sakeef Sani, Tasnim Jahan, Taufiq Hasan","submitted_at":"2023-06-25T08:23:44Z","abstract_excerpt":"The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods are powerful in learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the \"M"},"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":"2306.14169","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-25T08:23:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1702df017c675e3d1c05ffd26fddb627bda5e4761ff85f1079778d5f14713bc0","abstract_canon_sha256":"95695f2b5b1b66eab227d0a660221ed9c81eb981b27f6acfde1973632fed9c60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:32.408730Z","signature_b64":"QJmN2fNUBUjQhY+68vq/iI3uymPdtKKOpOuN9muCxr1+vF7MzYIZpyImKuFxPRraRjPNYL/sVhW2K7kAjxgfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c28aba8ca15072dfcb5604cad86e62b9f590578cc2436eb8366e40b448da7384","last_reissued_at":"2026-07-05T06:24:32.408182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:32.408182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anzirun Nahar Asma, Joydip Paul, Md. Tazuddin Ahmed, Nawsabah Noor, Shams Nafisa Ali, S. M. Sakeef Sani, Tasnim Jahan, Taufiq Hasan","submitted_at":"2023-06-25T08:23:44Z","abstract_excerpt":"The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods are powerful in learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the \"M"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.14169","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/2306.14169/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":"2306.14169","created_at":"2026-07-05T06:24:32.408241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.14169v1","created_at":"2026-07-05T06:24:32.408241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.14169","created_at":"2026-07-05T06:24:32.408241+00:00"},{"alias_kind":"pith_short_12","alias_value":"YKFLVDFBKBZN","created_at":"2026-07-05T06:24:32.408241+00:00"},{"alias_kind":"pith_short_16","alias_value":"YKFLVDFBKBZN7S2W","created_at":"2026-07-05T06:24:32.408241+00:00"},{"alias_kind":"pith_short_8","alias_value":"YKFLVDFB","created_at":"2026-07-05T06:24:32.408241+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.15915","citing_title":"An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH","json":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH.json","graph_json":"https://pith.science/api/pith-number/YKFLVDFBKBZN7S2WATFNQ3TCXH/graph.json","events_json":"https://pith.science/api/pith-number/YKFLVDFBKBZN7S2WATFNQ3TCXH/events.json","paper":"https://pith.science/paper/YKFLVDFB"},"agent_actions":{"view_html":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH","download_json":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH.json","view_paper":"https://pith.science/paper/YKFLVDFB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.14169&json=true","fetch_graph":"https://pith.science/api/pith-number/YKFLVDFBKBZN7S2WATFNQ3TCXH/graph.json","fetch_events":"https://pith.science/api/pith-number/YKFLVDFBKBZN7S2WATFNQ3TCXH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH/action/storage_attestation","attest_author":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH/action/author_attestation","sign_citation":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH/action/citation_signature","submit_replication":"https://pith.science/pith/YKFLVDFBKBZN7S2WATFNQ3TCXH/action/replication_record"}},"created_at":"2026-07-05T06:24:32.408241+00:00","updated_at":"2026-07-05T06:24:32.408241+00:00"}