Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning

• Autorzy: Sahoo Akshya Kumar , Parida Priyadarsan , Muralibabu K. , Dash Sonali

Identyfikatory

DOI

10.1016/j.bbe.2023.08.003

• Języki publikacji: EN

Abstrakty

EN

Brain tumors can be difficult to diagnose, as they may have similar radiographic characteristics, and a thorough examination may take a considerable amount of time. To address these challenges, we propose an intelligent system for the automatic extraction and identification of brain tumors from 2D CE MRI images. Our approach comprises two stages. In the first stage, we use an encoder-decoder based U-net with residual network as the backbone to detect different types of brain tumors, including glioma, meningioma, and pituitary tumors. Our method achieved an accuracy of 99.60%, a sensitivity of 90.20%, a specificity of 99.80%, a dice similarity coefficient of 90.11%, and a precision of 90.50% for tumor extraction. In the second stage, we employ a YOLO2 (you only look once) based transfer learning approach to classify the extracted tumors, achieving a classification accuracy of 97%. Our proposed approach outperforms state-of-the-art methods found in the literature. The results demonstrate the potential of our method to aid in the diagnosis and treatment of brain tumors.

Słowa kluczowe

EN

tumor segmentation; tumor classification; YOLO2; transfer learning; CNN

PL

segmentacja guza; YOLO2; transfer nauki; CNN

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 3
• Strony: 616--633
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Sahoo Akshya Kumar

• Department of Electrical and Electronics Engineering, GIET University, Gunupur, Odisha, India

autor

Parida Priyadarsan

• Department of Electronics and Communication Engineering, GIET University, Gunupur, Odisha, India

autor

Muralibabu K.

• Department of Electrical and Electronics Engineering, GIET University, Gunupur, Odisha, India

autor

Dash Sonali

• Department of Computer Science Engineering, Chandigarh University, Punjab, India

Bibliografia

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