Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics

Basit Öğe Kaydı
dc.authoridOZDEMIR, Cuneyt/0000-0002-9252-5888
dc.contributor.authorOzdemir, Cuneyt
dc.contributor.authorDogan, Yahya
dc.date.accessioned2024-12-24T19:24:51Z
dc.date.available2024-12-24T19:24:51Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThe early diagnosis of brain tumors is critical in the area of healthcare, owing to the potentially life-threatening repercussions unstable growths within the brain can pose to individuals. The accurate and early diagnosis of brain tumors enables prompt medical intervention. In this context, we have established a new model called MTAP to enable a highly accurate diagnosis of brain tumors. The MTAP model addresses dataset class imbalance by utilizing the ADASYN method, employs a network pruning technique to reduce unnecessary weights and nodes in the neural network, and incorporates Avg-TopK pooling method for enhanced feature extraction. The primary goal of our research is to enhance the accuracy of brain tumor type detection, a critical aspect of medical imaging and diagnostics. The MTAP model introduces a novel classification strategy for brain tumors, leveraging the strength of deep learning methods and novel model refinement techniques. Following comprehensive experimental studies and meticulous design, the MTAP model has achieved a state-of-the-art accuracy of 99.69%. Our findings indicate that the use of deep learning and innovative model refinement techniques shows promise in facilitating the early detection of brain tumors. Analysis of the model's heat map revealed a notable focus on regions encompassing the parietal and temporal lobes.
dc.description.sponsorshipSiirt University
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.1007/s11517-024-03064-5
dc.identifier.endpage2176
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue7
dc.identifier.pmid38483711
dc.identifier.scopus2-s2.0-85187707147
dc.identifier.scopusqualityQ2
dc.identifier.startpage2165
dc.identifier.urihttps://doi.org/10.1007/s11517-024-03064-5
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6154
dc.identifier.volume62
dc.identifier.wosWOS:001183601600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofMedical & Biological Engineering & Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectBrain tumor
dc.subjectADASYN
dc.subjectConvolutional neural network
dc.subjectPruning
dc.subjectAvg-TopK pooling
dc.titleAdvancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu