{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OWQHFWHAQMKAOOH3CFOGLXUJOW","short_pith_number":"pith:OWQHFWHA","schema_version":"1.0","canonical_sha256":"75a072d8e083140738fb115c65de8975b5bbb1f51ce989731d9404d2bd0ac60e","source":{"kind":"arxiv","id":"2304.10039","version":2},"attestation_state":"computed","paper":{"title":"Brain tumor multi classification and segmentation in MRI images using deep learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Belal Amin, Manar Ahmed, Mohamed Hassan, Mohammed Ahmed, Rana Ibrahim, Romario Sameh Samir, Youssef Tarek","submitted_at":"2023-04-20T01:32:55Z","abstract_excerpt":"This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in "},"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":"2304.10039","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2023-04-20T01:32:55Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"06cf0053e08fcd9aa9fe461f7f3d52d90c63cb9036414ce262c38de57e253612","abstract_canon_sha256":"6d829241bacce98f6f421a5c36733e7e4f4c3790b8c14519e9490326d36dbd53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:12.183277Z","signature_b64":"/ZdGlZr7fcytixN8rY20xooPAJEl0dYMVj1cfHP71zNkK8b/wR+1vjMmL0pmr5pGoWJyNLW/bTJO8uek6ybJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75a072d8e083140738fb115c65de8975b5bbb1f51ce989731d9404d2bd0ac60e","last_reissued_at":"2026-07-05T06:24:12.182834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:12.182834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brain tumor multi classification and segmentation in MRI images using deep learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Belal Amin, Manar Ahmed, Mohamed Hassan, Mohammed Ahmed, Rana Ibrahim, Romario Sameh Samir, Youssef Tarek","submitted_at":"2023-04-20T01:32:55Z","abstract_excerpt":"This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.10039","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/2304.10039/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":"2304.10039","created_at":"2026-07-05T06:24:12.182887+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.10039v2","created_at":"2026-07-05T06:24:12.182887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.10039","created_at":"2026-07-05T06:24:12.182887+00:00"},{"alias_kind":"pith_short_12","alias_value":"OWQHFWHAQMKA","created_at":"2026-07-05T06:24:12.182887+00:00"},{"alias_kind":"pith_short_16","alias_value":"OWQHFWHAQMKAOOH3","created_at":"2026-07-05T06:24:12.182887+00:00"},{"alias_kind":"pith_short_8","alias_value":"OWQHFWHA","created_at":"2026-07-05T06:24:12.182887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.07011","citing_title":"Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW","json":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW.json","graph_json":"https://pith.science/api/pith-number/OWQHFWHAQMKAOOH3CFOGLXUJOW/graph.json","events_json":"https://pith.science/api/pith-number/OWQHFWHAQMKAOOH3CFOGLXUJOW/events.json","paper":"https://pith.science/paper/OWQHFWHA"},"agent_actions":{"view_html":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW","download_json":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW.json","view_paper":"https://pith.science/paper/OWQHFWHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.10039&json=true","fetch_graph":"https://pith.science/api/pith-number/OWQHFWHAQMKAOOH3CFOGLXUJOW/graph.json","fetch_events":"https://pith.science/api/pith-number/OWQHFWHAQMKAOOH3CFOGLXUJOW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW/action/storage_attestation","attest_author":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW/action/author_attestation","sign_citation":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW/action/citation_signature","submit_replication":"https://pith.science/pith/OWQHFWHAQMKAOOH3CFOGLXUJOW/action/replication_record"}},"created_at":"2026-07-05T06:24:12.182887+00:00","updated_at":"2026-07-05T06:24:12.182887+00:00"}