Analysis of brain tumor using MRI images

• Autorzy: Uberg Lewi L. , Kadry Seifedine

Identyfikatory

DOI

10.15439/2022F69

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The increasing rates of deadly brain tumors inhumans correspondingly increase the need for highly experienced medical personnel for diagnosis and treatment. Therefore, to reduce the workload and the time from suspicion of disease to diagnosis, then plan for suitable treatment, there is a need to automate the initial part of the process by implementing a Computer-Aided-Disease-Diagnosis (CADD) system for brain tumor classification. By studying the types of tumors involved, how the convolutional neural network works, some of its pretrained models, and their application in brain tumor classification, the likelihood of producing a promising CADD system heavily increases. The research shows that the DenseNet121 architecture, either fully trained or using transfer learning, likely is the most appropriate candidate for the CADD system in development.

Słowa kluczowe

EN

brain tumor classification; medical imaging; CADD; convolutional neural network; machine learning; deep learning

PL

klasyfikacja; guz mózgu; obrazowanie medyczne; CADD; konwolucyjna sieć neuronowa; uczenie maszynowe; głębokie uczenie

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 201--204
• Opis fizyczny: Bibliogr. 17 poz., il., tab.

Twórcy

autor

Uberg Lewi L.

• Applied Data Science Noroff University College Kristiansand, Norway

autor

Kadry Seifedine

• Applied Data Science Noroff University College Kristiansand, Norway

Bibliografia

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Uwagi

1. Short article

2. Track 3: 4th International Workshop on Artificial Intelligence in Machine Vision and Graphics

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).