BRAIN TUMOR CLASSIFICATION

Registration ID: IJNRD_200928

Published ID: IJNRD2307491

DOI: Click Here to Get

Darsh Lukkad , Om Gujarathi , Manaswi Lukkad

Tumor, MRI, CNN

Brain tumours are among the most deadly and difficult to treat cancers, so early identification is crucial for enhancing patient outcomes. Recently, deep learning techniques have shown great promise in the identification and categorization of brain tumours from medical imaging data. In this investigation, we look at the use of deep learning methods for MRI brain tumour detection. We develop a convolutional neural network that can identify and classify several types of brain tumours based on their characteristics. We evaluate the performance of our model using a publicly available dataset of brain tumours and contrast it with other state-of the-art techniques. Our results show that the suggested strategy works better and achieves excellent accuracy than other methods already in use. In order to improve patient outcomes and survival rates, our research shows the potential of deep learning approaches for enhancing the identification and diagnosis of brain tumours. The proposed method could be used in clinical settings to help in the early detection of brain tumours. Brain tumours must be discovered early for better patient outcomes.

Published Paper   E-Certificate

Darsh Lukkad, Om Gujarathi, & Manaswi Lukkad (July-2023). BRAIN TUMOR CLASSIFICATION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(7), e762-e771. https://ijnrd.org/papers/IJNRD2307491.pdf

Volume 8 Issue 7, July-2023

Pages : e762-e771

Paper Reg. ID: IJNRD_200928

Published Paper Id: IJNRD2307491

Downloads: 000121979

Research Area: Computer Science & Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307491.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2307491

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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