Review and potential for artificial intelligence in healthcare

被引:15
|
作者
Sun, Lina [1 ]
Gupta, Rajiv Kumar [2 ]
Sharma, Amit [3 ]
机构
[1] North China Inst Sci & Technol, Langfang, Hebei, Peoples R China
[2] Pandit Deendayal Energy Univ, Gandhinagar, India
[3] Chitkara Univ, Dept Comp Sci & Engn, Rajpura, Punjab, India
关键词
Segmentation; Image processing; Brain tumor; Magnetic resonance imaging; Irregular cell population; Brain anatomy; BRAIN-TUMOR SEGMENTATION; OF-THE-ART; IMAGE SEGMENTATION; MRI SEGMENTATION; ALGORITHM; MODEL;
D O I
10.1007/s13198-021-01221-9
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the medical image analysis, recognition of tumor in brain is very important task and it leads cancer which should be diagnosed at early stage. It is an irregular cell population in brain and for the cancer diagnosis; medical imaging techniques play an important role. The mostly used and efficient technique for the segmentation is Magnetic resonance imaging (MRI). There is huge progress the field of MRI imaging technique for accessing the brain injury and the brain anatomy exploring. The segmentation and detection of the tumor from the MRI images are done by the image processing techniques. Manual detection of brain tumor is the complex task, so the different image segmentation methods are developed for detection and segmentation of the tumor from the MRI images. The various recent brain tumor segmentation techniques are thoroughly discussed in this paper. The quantitative analysis of existing techniques and the performance evaluation is done and detailed. The paper revealed different image segmentation methods are briefly discussed. This survey article provides the detailed information of the different segmentation methods along with their merits and demerits. Effectiveness of the methods is shown in terms of the performance parameters.
引用
收藏
页码:54 / 62
页数:9
相关论文
共 50 条
  • [1] Review and potential for artificial intelligence in healthcare
    Lina Sun
    Rajiv Kumar Gupta
    Amit Sharma
    [J]. International Journal of System Assurance Engineering and Management, 2022, 13 : 54 - 62
  • [2] The potential for artificial intelligence in healthcare
    Wen, Zuocheng
    Huang, Hua
    [J]. Journal of Commercial Biotechnology, 2022, 27 (04) : 217 - 224
  • [3] A review of Explainable Artificial Intelligence in healthcare
    Sadeghi, Zahra
    Alizadehsani, Roohallah
    Cifci, Mehmet Akif
    Kausar, Samina
    Rehman, Rizwan
    Mahanta, Priyakshi
    Bora, Pranjal Kumar
    Almasri, Ammar
    Alkhawaldeh, Rami S.
    Hussain, Sadiq
    Alatas, Bilal
    Shoeibi, Afshin
    Moosaei, Hossein
    Hladik, Milan
    Nahavandi, Saeid
    Pardalos, Panos M.
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2024, 118
  • [4] ARTIFICIAL INTELLIGENCE IN HEALTHCARE : A BRIEF REVIEW
    Kumar, Chiguruvada Ramakrishnan Punith
    Natarajan, Ramalakshmi
    Padma, Kumar
    Sivaperuman, Amuthalakshmi
    [J]. SURANAREE JOURNAL OF SCIENCE AND TECHNOLOGY, 2022, 29 (02):
  • [5] A Review of the Role of Artificial Intelligence in Healthcare
    Al Kuwaiti, Ahmed
    Nazer, Khalid
    Al-Reedy, Abdullah
    Al-Shehri, Shaher
    Al-Muhanna, Afnan
    Subbarayalu, Arun Vijay
    Al Muhanna, Dhoha
    Al-Muhanna, Fahad A.
    [J]. JOURNAL OF PERSONALIZED MEDICINE, 2023, 13 (06):
  • [6] The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare
    Aung, Yuri Y. M.
    Wong, David C. S.
    Ting, Daniel S. W.
    [J]. BRITISH MEDICAL BULLETIN, 2021, 139 (01) : 4 - 15
  • [7] Artificial intelligence in elderly healthcare: A scoping review
    Ma, Bingxin
    Yang, Jin
    Wong, Frances Kam Yuet
    Wong, Arkers Kwan Ching
    Ma, Tingting
    Meng, Jianan
    Zhao, Yue
    Wang, Yaogang
    Lu, Qi
    [J]. AGEING RESEARCH REVIEWS, 2023, 83
  • [8] Fairness of artificial intelligence in healthcare: review and recommendations
    Daiju Ueda
    Taichi Kakinuma
    Shohei Fujita
    Koji Kamagata
    Yasutaka Fushimi
    Rintaro Ito
    Yusuke Matsui
    Taiki Nozaki
    Takeshi Nakaura
    Noriyuki Fujima
    Fuminari Tatsugami
    Masahiro Yanagawa
    Kenji Hirata
    Akira Yamada
    Takahiro Tsuboyama
    Mariko Kawamura
    Tomoyuki Fujioka
    Shinji Naganawa
    [J]. Japanese Journal of Radiology, 2024, 42 : 3 - 15
  • [9] Fairness of artificial intelligence in healthcare: review and recommendations
    Ueda, Daiju
    Kakinuma, Taichi
    Fujita, Shohei
    Kamagata, Koji
    Fushimi, Yasutaka
    Ito, Rintaro
    Matsui, Yusuke
    Nozaki, Taiki
    Nakaura, Takeshi
    Fujima, Noriyuki
    Tatsugami, Fuminari
    Yanagawa, Masahiro
    Hirata, Kenji
    Yamada, Akira
    Tsuboyama, Takahiro
    Kawamura, Mariko
    Fujioka, Tomoyuki
    Naganawa, Shinji
    [J]. JAPANESE JOURNAL OF RADIOLOGY, 2024, 42 (01) : 3 - 15
  • [10] Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector
    Bangul khan
    Hajira Fatima
    Ayatullah Qureshi
    Sanjay Kumar
    Abdul Hanan
    Jawad Hussain
    Saad Abdullah
    [J]. Biomedical Materials & Devices, 2023, 1 (2): : 731 - 738