An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks. (15th March 2022)
- Record Type:
- Journal Article
- Title:
- An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks. (15th March 2022)
- Main Title:
- An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks
- Authors:
- Han-Trong, Thanh
Nguyen Van, Hinh
Nguyen Thi Thanh, Huong
Tran Anh, Vu
Nguyen Tuan, Dung
Vu Dang, Luu - Other Names:
- Bardhan Abidhan Academic Editor.
- Abstract:
- Abstract : This paper proposes a system to effectively identify brain tumors on MRI images using artificial intelligence algorithms and ADAS optimization function. This system is developed with the aim of assisting doctors in diagnosing one of the most dangerous diseases for humans. The data used in the study is patient image data collected from Bach Mai Hospital, Vietnam. The proposed approach includes two main steps. First, we propose the normalization method for brain MRI images to remove unnecessary components without affecting their information content. In the next step, Deep Convolutional Neural Networks are used and then we propose to apply ADAS optimization function to build predictive models based on that normalized dataset. From there, the results will be compared to choose the most optimal method. Those results of the evaluated algorithms through the coefficient F1-score are greater than 94% and the highest value is 97.65%.
- Is Part Of:
- Applied computational intelligence and soft computing. Volume 2022(2022)
- Journal:
- Applied computational intelligence and soft computing
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-03-15
- Subjects:
- Computational intelligence -- Periodicals
Soft computing -- Periodicals
006.305 - Journal URLs:
- https://www.hindawi.com/journals/acisc/ ↗
- DOI:
- 10.1155/2022/2092985 ↗
- Languages:
- English
- ISSNs:
- 1687-9724
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 21436.xml