Cancer Detection Based on Medical Image Analysis with the Help of Machine Learning and Deep Learning Techniques: A Systematic Literature Review

被引:3
作者
Sood, Tamanna [1 ,2 ]
Bhatia, Rajesh [1 ]
Khandnor, Padmavati [1 ]
机构
[1] Punjab Engn Coll Deemed Univ, Dept Comp Sci & Engn, Chandigarh, India
[2] Punjab Engn Coll, Comp Sci & Engn, Chandigarh, India
关键词
Medical image analysis; cancer detection; machine learning; deep learning; medical images; CONVOLUTIONAL NEURAL-NETWORK; COMPUTER-AIDED DIAGNOSIS; GENERATIVE ADVERSARIAL NETWORK; BREAST-CANCER; CT IMAGES; ULTRASOUND IMAGES; ARTIFICIAL-INTELLIGENCE; DIGITAL MAMMOGRAMS; NODULE DIAGNOSIS; CLASSIFICATION;
D O I
10.2174/1573405619666230217100130
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background Cancer is a deadly disease. It is crucial to diagnose cancer in its early stages. This can be done with medical imaging. Medical imaging helps us scan and view internal organs. The analysis of these images is a very important task in the identification and classification of cancer. Over the past years, the occurrence of cancer has been increasing, so has been the load on the medical fraternity. Fortunately, with the growth of Artificial Intelligence in the past decade, many tools and techniques have emerged which may help doctors in the analysis of medical images. Methodology This is a systematic study covering various tools and techniques used for medical image analysis in the field of cancer detection. It focuses on machine learning and deep learning technologies, their performances, and their shortcomings. Also, the various types of imaging techniques and the different datasets used have been discussed extensively. This work also discusses the various pre-processing techniques that have been performed on medical images for better classification. Results A total of 270 studies from 5 different publications and 5 different conferences have been included and compared on the above-cited parameters. Conclusion Recommendations for future work have been given towards the end.
引用
收藏
页码:1487 / 1522
页数:36
相关论文
共 268 条
  • [91] Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Hirasawa, Toshiaki
    Aoyama, Kazuharu
    Tanimoto, Tetsuya
    Ishihara, Soichiro
    Shichijo, Satoki
    Ozawa, Tsuyoshi
    Ohnishi, Tatsuya
    Fujishiro, Mitsuhiro
    Matsuo, Keigo
    Fujisaki, Junko
    Tada, Tomohiro
    [J]. GASTRIC CANCER, 2018, 21 (04) : 653 - 660
  • [92] Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach
    Hoa Hoang Ngoc Pham
    Futakuchi, Mitsuru
    Bychkov, Andrey
    Furukawa, Tomoi
    Kuroda, Kishio
    Fukuoka, Junya
    [J]. AMERICAN JOURNAL OF PATHOLOGY, 2019, 189 (12) : 2428 - 2439
  • [93] Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks
    Horie, Yoshimasa
    Yoshio, Toshiyuki
    Aoyama, Kazuharu
    Yoshimizu, Shoichi
    Horiuchi, Yusuke
    Ishiyama, Akiyoshi
    Hirasawa, Toshiaki
    Tsuchida, Tomohiro
    Ozawa, Tsuyoshi
    Ishihara, Soichiro
    Kumagai, Youichi
    Fujishiro, Mitsuhiro
    Maetani, Iruru
    Fujisaki, Junko
    Tada, Tomohiro
    [J]. GASTROINTESTINAL ENDOSCOPY, 2019, 89 (01) : 25 - 32
  • [94] Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks
    Hosny, Khalid M.
    Kassem, Mohamed A.
    Foaud, Mohamed M.
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (33-34) : 24029 - 24055
  • [95] Deep learning for image-based cancer detection and diagnosis - A survey
    Hu, Zilong
    Tang, Jinshan
    Wang, Ziming
    Zhang, Kai
    Zhang, Ling
    Sun, Qingling
    [J]. PATTERN RECOGNITION, 2018, 83 : 134 - 149
  • [96] Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images
    Huang, Yunzhi
    Han, Luyi
    Dou, Haoran
    Luo, Honghao
    Yuan, Zhen
    Liu, Qi
    Zhang, Jiang
    Yin, Guangfu
    [J]. BIOMEDICAL ENGINEERING ONLINE, 2019, 18 (1)
  • [97] Generative Adversarial Network for Medical Images (MI-GAN)
    Iqbal, Talha
    Ali, Hazrat
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2018, 42 (11)
  • [98] An enhanced deep learning approach for brain cancer MRI images classification using residual networks
    Ismael, Sarah Ali Abdelaziz
    Mohammed, Ammar
    Hefny, Hesham
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2020, 102
  • [99] Computer aided diagnostic system for ultrasound liver images: A systematic review
    Jabarulla, Mohamed Yaseen
    Lee, Heung-No
    [J]. OPTIK, 2017, 140 : 1114 - 1126
  • [100] Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)
    Jalalian, Afsaneh
    Mashohor, Syamsiah
    Mahmud, Rozi
    Karasfi, Babak
    Saripan, M. Iqbal
    Ramli, Abdul Rahman
    [J]. JOURNAL OF DIGITAL IMAGING, 2017, 30 (06) : 796 - 811