Exploring Deep Learning-based Approaches for Brain Tumor Diagnosis from MRI Images

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dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorAbdullah, Fasih
dc.contributor.authorJamil, Akhtar
dc.contributor.authorAlazawi, Esraa Mohammed
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2025-04-18T06:16:24Z
dc.date.available2025-04-18T06:16:24Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractBrain tumors significantly impair health due to their aggressive growth, necessitating rapid and accurate detection and classification for effective treatment. Traditional methods relying on medical professionals' manual MRI analysis are time-consuming and resource-intensive. This study leverages Artificial Intelligence-based deep learning approaches to streamline and enhance the accuracy of brain tumor classification from MRI images. We evaluated five advanced deep learning models: a custom CNN, DeepTumor-Net, VGG-16, ResNet-50, and Xception. These models were applied to the 'Brain tumors 256×256' dataset, classifying brain tumors into four distinct categories: no tumor, Glioma, Meningioma, and Pituitary Tumors. These models were trained and then fine-tuned, which further refined their performance. The models were evaluated using the standard evaluation metrics, including accuracy, precision, recall, F1-Score, specificity, and Cohen's Kappa. The final results showed that high accuracies can be obtained for MRI classification using these deep learning models. Notably, ResNet-50 and VGG-16 stood out with test accuracies of 92.6 % and 92.1 %, respectively, indicating their significant potential in medical imaging analysis. © 2024 IEEE.
dc.description.sponsorshipCentral Michigan University (CMU)IEEE
dc.identifier.citationAbdullah, F., Jamil, A., Alazawi, E. M., & Hameed, A. A. (2024, April). Exploring Deep Learning-based Approaches for Brain Tumor Diagnosis from MRI Images. In 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-11). IEEE.
dc.identifier.doi10.1109/ICMI60790.2024.10585851
dc.identifier.isbn979-835037297-7
dc.identifier.scopus2-s2.0-85199459362
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6324
dc.indekslendigikaynakScopus
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBrain Tumor Classification
dc.subjectConvolutional Neural Networks
dc.subjectDeep Learning
dc.subjectMedical Image Analysis
dc.subjectModel Fine Tuning
dc.titleExploring Deep Learning-based Approaches for Brain Tumor Diagnosis from MRI Images
dc.typeConference Object

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