The Application of Artificial Intelligence Technology for the Diagnosis of Malignant Tumors: A Review
DOI:
https://doi.org/10.52783/jns.v14.3618Keywords:
Global warming, Green house gases, Climate change, Sustainability, IPCCAbstract
Background: The digital technology has revolutionized many fields including the healthcare. The advancement in digital technology in the form of artificial intelligence is going to revamp the patient care in near future. The application of convolutional neural network in the computer aided diagnostic system has revealed very promising results.
Material and methods: The research papers were collected after searching the databases by using the specific key words such as convolutional network (CNN), ResNet34, ResNet50, ResNet101, ResNet152, EfficientNet B3 and VGG-19. The search period was five years from 2020 to 2024.
Results: A total of 33 research article have been selected for this review paper on the basis of inclusion and exclusion criteria. The significant majority of the studies revealed that diagnostic accuracy of artificial intelligence technology regarding the histopathological diagnosis of malignant tumors ranged from 85% to 100%.
Discussion: The computer aided diagnostic system based on artificial intelligence technology has emerged as excellent technique for the histopathological examination of the tissue specimens. In some conditions, it has surpassed the pathologist in the speedy evaluation of lesions with more diagnostic accuracy.
Conclusion: The application of artificial intelligence for the diagnosis of the malignant tumors could provide a very valuable assistance to the pathologist. It would provide more speedy results with high accuracy which could be very important in the patient care
Downloads
Metrics
References
Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024; 74(3): 229-263. doi:10.3322/caac.21834
Dai X, Gakidou E, Lopez AD. Evolution of the global smoking epidemic over the past half century: strengthening the evidence base for policy action. Tobacco Control. 2022;31:129-137.
Boutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism. 2022 ;133:155217. doi: 10.1016/j.metabol.2022.155217.
Pati S, Irfan W, Jameel A, Ahmed S, Shahid RK. Obesity and Cancer: A Current Overview of Epidemiology, Pathogenesis, Outcomes, and Management. Cancers (Basel). 2023;15(2):485. doi:10.3390/cancers15020485
Jun S, Park H, Kim UJ, Choi EJ, Lee HA, Park B et al.Cancer risk based on alcohol consumption levels: a comprehensive systematic review and meta-analysis. Epidemiol Health. 2023;45:e2023092. DOI: https://doi.org/10.4178/epih.e2023092
Novianti TE, Rachmah Q, Adriani M. The Effect of Low-Fiber Diets on Colorectal Cancer Incidence in Southeast and East Asia: Systematic Review And Meta-Analysis. The Indonesian Journal of Public Health. 2023:18(2); 353-365. https://doi.org/10.20473/Ijph.v18i2.2023.353-365.
Turner MC, Andersen ZJ, Baccarelli A, Diver WR, Gapstur SM, Pope CA 3rd, Prada D, Samet J, Thurston G, Cohen A. Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations. CA Cancer J Clin. 2020:25;10.3322/caac.21632. doi: 10.3322/caac.21632.
Khatab Z, Hanna K, Rofaeil A, Wang C, Maung R, Yousaf GM. Pathologist workload, burn out and wellness: connecting the dots. Critical Review in clinical laboratory sciences. 2023:51(4); 254-274.
Sidik A I, Komarov R N, Gawusu S, et al. Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis. Cureus. 2024; 16(8): e66925. doi:10.7759/cureus.66925
Zhang B, Shi H, Wang H. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach. J Multidiscip Healthc. 2023;16:1779-1791. doi: 10.2147/JMDH.S410301.
Zhang, C., Xu, J., Tang, R. et al. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol .2023:16, 114 . https://doi.org/10.1186/s13045-023-01514-5
Hunter B, Hindocha S, Lee RW. The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers (Basel). 2022;14(6):1524. doi: 10.3390/cancers14061524.
Arshad W, Masood T, Mahmood T, Jaffar A, Alamni FS, Bahaj SAO et al. Cancer Unveiled: A Deep Dive Into Breast Tumor Detection Using Cutting-Edge Deep Learning Models, IEEE Access.2023:11:133804-133824. doi: 10.1109/ACCESS.2023.3335604.
Ibrahim DM, Hammoudeh MAA, Allam TM. Histopathological cancer detection based on deep learning and stain images. Indonesian Journal of Electrical Engineering and Computer Science.2024:36(1);214-230. DOI: 10.11591/ijeecs.v36.i1.pp214-230
Bukhari SUK, Mehtab U, Hussain SS, Armaghan SU, Syed A, Shah SSH. The Assessment of deep learning Computer Vision Algorithms for the Diagnosis of Prostatic adenocarcinoma . Ann Clin Anal Med 2021; 12(suppl 1): S31-34 DOI: 10.4328/ACAM.20322
Bukhari SUK, Bokhari SKA, Syed A, Hussain SS, Armaghan SU, Shah SSH. The Diagnostic Accuracy of Convolutional Neural Network Architectures For The Diagnosis of Brain Cancer. P J M H S Vol. 14, NO. 3, July –Sept 2020: 1037-39.
Perry C, Greenberg O, Haberman S, Herskovitz N, Gazy I, Avinoam A, et al. Image-based deep learning detection of high-grade b-cell lymphomas directly from hematoxylin and eosin images. Cancers (Basel). Oct 29, 2023;15(21):5205.
Raciti P, Sue J, Retamero JA, Ceballos R, Godrich R, Kunz JD, et al. Clinical validation of artificial intelligence-augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection. Arch Pathol Lab Med. 2023;147(10):1178-1185.
Steinbuss G, Kriegsmann M, Zgorzelski C, Brobeil A, Goeppert B, Dietrich S, et al. Deep learning for the classification of non-Hodgkin lymphoma on histopathological images. Cancers (Basel). May 17, 2021;13(10):2419.
Miyoshi H, Sato K, Kabeya Y, Yonezawa S, Nakano H, Takeuchi Y, et al. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma. Lab Invest. Oct 29, 2020;100(10):1300-1310
Syrykh C, Abreu A, Amara N, Siegfried A, Maisongrosse V, Frenois FX, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digit Med. 2020;3:63.
Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, et al. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. Nov 26, 2020;11(1):6004
Zhang X, Zhang K, Jiang M, Yang L. Research on the classification of lymphoma pathological images based on deep residual neural network. Technol Health Care. 2021;29(S1):335-344.
Yu W, Li C, Wang R, Yeh C, Chuang S. Machine learning based on morphological features enables classification of primary intestinal t-cell lymphomas. Cancers (Basel). Oct 30, 2021;13(21):5463
Hamdi M, Senan EM, Jadhav ME, Olayah F, Awaji B, Alalayah KM. Hybrid models based on fusion features of a CNN and handcrafted features for accurate histopathological image analysis for diagnosing malignant lymphomas. Diagnostics (Basel). Jul 04, 2023;13(13):2258.
Wang J, Liu Q, Xie H, Yang Z, Zhou H. Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks. Cancers. 2021: 13(4); 661. doi.org/10.3390/cancers13040661
Balasubramanian AA, Al-Heejawi SMA, Singh A, Breggia A, Ahmad B, Christman R, Ryan ST, Amal S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers (Basel). 2024 Jun 14;16(12):2222. doi: 10.3390/cancers16122222.
Sarwinda D, Paradisa RH, Bustamam A, Anggia P,. Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Computer Science,. 2021:179;423-431. https://doi.org/10.1016/j.procs.2021.01.025
Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–263. https://doi.org/10.3322/caac.21834
Kim I, Kang K, Song Y, Kim TJ. Application of Artificial Intelligence in Pathology: Trends and Challenges. Diagnostics (Basel). 2022:15;12(11):2794. doi: 10.3390/diagnostics12112794.
Greeley C, Holder L, Nilsson EE, Skinner MK. Scalable deep learning artificial intelligence histopathology slide analysis and validation. Sci Rep. 2024 ;14(1):26748. doi: 10.1038/s41598-024-76807-x.
Steiner DF, Nagpal K, Sayres R, Foote DJ, Wedin BD, Pearce A, et al. Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies. JAMA Netw Open. 2020 ;3(11):e2023267. doi: 10.1001/jamanetworkopen.2020.23267.
Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol.2023: 18; 109 . https://doi.org/10.1186/s13000-023-01375-z
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
