{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YFSVBTSYLWU6UA53JJVS7XJH7R","short_pith_number":"pith:YFSVBTSY","schema_version":"1.0","canonical_sha256":"c16550ce585da9ea03bb4a6b2fdd27fc49a9c29d22d0a3e84a729066a16aef45","source":{"kind":"arxiv","id":"2001.01953","version":1},"attestation_state":"computed","paper":{"title":"Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Bunyarit Uyyanonvara, Chanjira Sinthanayothin, Faisal Ghaffar, Hirohiko Kaneko, Sarwar Khan","submitted_at":"2020-01-07T10:20:11Z","abstract_excerpt":"This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, haemorrhages and microaneurysms. To grade the disease the screening system must efficiently detect these features. In this paper we are proposing a simple and fast method for detection of diabetic retinopathy. We do pre-processing of grey-scale image and find all labelled connected components (blobs) in an image regardless of whether it is haemorrhages,"},"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":"2001.01953","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-01-07T10:20:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9ec7dfa4c72037d120bfcb352156546337521ace58d8e68a137fbf388216a0e7","abstract_canon_sha256":"759bb1ae8cfe9644d85aac42f68f76b876069329750b6486e321bb64671c8d70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:30:42.623312Z","signature_b64":"VySlcNgiVEeP5bx+8w3XgwEp05CB1YcvuTplxzbJ+4hFsaYtfAEHLnW4LBpf9IH+enQqnBOlckDGe02xeuMbAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c16550ce585da9ea03bb4a6b2fdd27fc49a9c29d22d0a3e84a729066a16aef45","last_reissued_at":"2026-07-05T00:30:42.622959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:30:42.622959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Bunyarit Uyyanonvara, Chanjira Sinthanayothin, Faisal Ghaffar, Hirohiko Kaneko, Sarwar Khan","submitted_at":"2020-01-07T10:20:11Z","abstract_excerpt":"This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, haemorrhages and microaneurysms. To grade the disease the screening system must efficiently detect these features. In this paper we are proposing a simple and fast method for detection of diabetic retinopathy. We do pre-processing of grey-scale image and find all labelled connected components (blobs) in an image regardless of whether it is haemorrhages,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.01953","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/2001.01953/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":"2001.01953","created_at":"2026-07-05T00:30:42.623027+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.01953v1","created_at":"2026-07-05T00:30:42.623027+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.01953","created_at":"2026-07-05T00:30:42.623027+00:00"},{"alias_kind":"pith_short_12","alias_value":"YFSVBTSYLWU6","created_at":"2026-07-05T00:30:42.623027+00:00"},{"alias_kind":"pith_short_16","alias_value":"YFSVBTSYLWU6UA53","created_at":"2026-07-05T00:30:42.623027+00:00"},{"alias_kind":"pith_short_8","alias_value":"YFSVBTSY","created_at":"2026-07-05T00:30:42.623027+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/YFSVBTSYLWU6UA53JJVS7XJH7R","json":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R.json","graph_json":"https://pith.science/api/pith-number/YFSVBTSYLWU6UA53JJVS7XJH7R/graph.json","events_json":"https://pith.science/api/pith-number/YFSVBTSYLWU6UA53JJVS7XJH7R/events.json","paper":"https://pith.science/paper/YFSVBTSY"},"agent_actions":{"view_html":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R","download_json":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R.json","view_paper":"https://pith.science/paper/YFSVBTSY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.01953&json=true","fetch_graph":"https://pith.science/api/pith-number/YFSVBTSYLWU6UA53JJVS7XJH7R/graph.json","fetch_events":"https://pith.science/api/pith-number/YFSVBTSYLWU6UA53JJVS7XJH7R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R/action/storage_attestation","attest_author":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R/action/author_attestation","sign_citation":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R/action/citation_signature","submit_replication":"https://pith.science/pith/YFSVBTSYLWU6UA53JJVS7XJH7R/action/replication_record"}},"created_at":"2026-07-05T00:30:42.623027+00:00","updated_at":"2026-07-05T00:30:42.623027+00:00"}