{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BP54C7TS5V43LS7PI6JIIWJGBC","short_pith_number":"pith:BP54C7TS","schema_version":"1.0","canonical_sha256":"0bfbc17e72ed79b5cbef47928459260897276167460ceafa2e898127f3d688d2","source":{"kind":"arxiv","id":"2605.12560","version":1},"attestation_state":"computed","paper":{"title":"Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A lightweight CNN classifies brain tumors in MRI images at 99 percent accuracy using far fewer parameters than standard models.","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Jannatul Ferdous, Md Fahimul Kabir Chowdhury","submitted_at":"2026-05-11T21:39:24Z","abstract_excerpt":"Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models that are now in use are computationally intensive and have difficulty handling the intrinsic complexity and variety of different types of brain tumors. In this work, we propose a lightweight yet high-performing Convolutional Neural Network (CNN) for multi-class brain tumor classification, employing MRI images to target gliomas, meningiomas, pituitary tumors, and healthy (no tumo"},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.12560","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-05-11T21:39:24Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"9d406ecaa863693b81d70342bb4408dd21c435c3506f3ead22e74a128e689383","abstract_canon_sha256":"1c3c39220dd671aa72c81a0017c5c8bd044f3c97a4cb753cbd3979afc48b5a12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:10:01.993356Z","signature_b64":"+COjaLK+XWZt9B1j1Pr8O2r02DBJX+haGEr/4wpW1aQpREif3B7CBqZoLQDhExeHYpw37v2kyp4A/A9SGzScCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bfbc17e72ed79b5cbef47928459260897276167460ceafa2e898127f3d688d2","last_reissued_at":"2026-05-18T03:10:01.992699Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:10:01.992699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A lightweight CNN classifies brain tumors in MRI images at 99 percent accuracy using far fewer parameters than standard models.","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Jannatul Ferdous, Md Fahimul Kabir Chowdhury","submitted_at":"2026-05-11T21:39:24Z","abstract_excerpt":"Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models that are now in use are computationally intensive and have difficulty handling the intrinsic complexity and variety of different types of brain tumors. In this work, we propose a lightweight yet high-performing Convolutional Neural Network (CNN) for multi-class brain tumor classification, employing MRI images to target gliomas, meningiomas, pituitary tumors, and healthy (no tumo"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our CNN achieved classification accuracies of 99.03% and 99.28%, along with ROC scores of 99.88% and 99.94% on Dataset 1 and Dataset 2, respectively-all while utilizing significantly fewer parameters than popular pre-trained architectures.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The high reported accuracies will generalize to new MRI scans from different hospitals, scanners, or patient populations, without evidence of external validation or safeguards against overfitting on the two chosen datasets.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A custom lightweight CNN classifies brain tumors in MRI scans at 99%+ accuracy on two public datasets while using fewer parameters than standard models like ResNet50.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A lightweight CNN classifies brain tumors in MRI images at 99 percent accuracy using far fewer parameters than standard models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f873f56a59f9d829a5fb36ffd6928a16c0f8bce836c9e38d998b3ea9313b99f1"},"source":{"id":"2605.12560","kind":"arxiv","version":1},"verdict":{"id":"67dbc7df-87cb-4234-9045-4d635a06dd36","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T20:42:44.828613Z","strongest_claim":"our CNN achieved classification accuracies of 99.03% and 99.28%, along with ROC scores of 99.88% and 99.94% on Dataset 1 and Dataset 2, respectively-all while utilizing significantly fewer parameters than popular pre-trained architectures.","one_line_summary":"A custom lightweight CNN classifies brain tumors in MRI scans at 99%+ accuracy on two public datasets while using fewer parameters than standard models like ResNet50.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The high reported accuracies will generalize to new MRI scans from different hospitals, scanners, or patient populations, without evidence of external validation or safeguards against overfitting on the two chosen datasets.","pith_extraction_headline":"A lightweight CNN classifies brain tumors in MRI images at 99 percent accuracy using far fewer parameters than standard models."},"references":{"count":22,"sample":[{"doi":"","year":2019,"title":"Cancer diagnosis using deep learning: a bibliographic review,","work_id":"19877782-e169-4209-9a5e-c3c2b52deb05","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Rehabilitation of adult patients with primary brain tumors: a narrative review,","work_id":"01c57685-a1f2-4631-a0d8-06508de769f5","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Multiple brain tumor classification with dense cnn architecture using brain mri images,","work_id":"6ddda27c-e3f5-48f1-af22-39b1a23c31f1","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Development of a smart system for neonatal jaundice detection using cnn algorithm,","work_id":"6e57941e-71a6-48ee-b95b-d9aede43c1a2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Deep transfer learning approaches in performance analysis of brain tumor classification using mri images,","work_id":"2d3ba09f-69f4-4b97-81fa-490b98a1bbc1","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":22,"snapshot_sha256":"8a4b48028a03bc6ce1b35840382c1c635f1649af12e3ee724ee6420b6e42b2e7","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":"2605.12560","created_at":"2026-05-18T03:10:01.992814+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.12560v1","created_at":"2026-05-18T03:10:01.992814+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12560","created_at":"2026-05-18T03:10:01.992814+00:00"},{"alias_kind":"pith_short_12","alias_value":"BP54C7TS5V43","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"BP54C7TS5V43LS7P","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"BP54C7TS","created_at":"2026-05-18T12:33:37.589309+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/BP54C7TS5V43LS7PI6JIIWJGBC","json":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC.json","graph_json":"https://pith.science/api/pith-number/BP54C7TS5V43LS7PI6JIIWJGBC/graph.json","events_json":"https://pith.science/api/pith-number/BP54C7TS5V43LS7PI6JIIWJGBC/events.json","paper":"https://pith.science/paper/BP54C7TS"},"agent_actions":{"view_html":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC","download_json":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC.json","view_paper":"https://pith.science/paper/BP54C7TS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.12560&json=true","fetch_graph":"https://pith.science/api/pith-number/BP54C7TS5V43LS7PI6JIIWJGBC/graph.json","fetch_events":"https://pith.science/api/pith-number/BP54C7TS5V43LS7PI6JIIWJGBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC/action/storage_attestation","attest_author":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC/action/author_attestation","sign_citation":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC/action/citation_signature","submit_replication":"https://pith.science/pith/BP54C7TS5V43LS7PI6JIIWJGBC/action/replication_record"}},"created_at":"2026-05-18T03:10:01.992814+00:00","updated_at":"2026-05-18T03:10:01.992814+00:00"}