Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors

Arieshanti, Isye; Purwananto, Yudhi; Tjandrasa, Handayani

doi:10.12928/telkomnika.v11i4.1203

Cited by 2 publications

(1 citation statement)

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“…The development of ovarian cancer prediction came through surface-enhanced laser-desorption & lionization-time of flight-mass spectrometry data on given dataset. The purposed methods give more accurate results than others give and obtained highest accuracy [76].…”

Section: Feasible Studies and Purposed Methods For Ovarian Cancermentioning

confidence: 89%

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Sajjad

Khan

Nawaz

et al. 2022

JCBI

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Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

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Section: Feasible Studies and Purposed Methods For Ovarian Cancermentioning

confidence: 89%