Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An Insight into Machine Learning Techniques for Cancer Detection
Cancer is a dreadful disease and a primary cause of death. Early cancer detection and screening are critical for enhancing survival rates. Conventional cancer diagnostic techniques have a number of disadvantages, including being more expensive, error-prone, and time-consuming. To address this issue, machine learning has been widely used in cancer detection and diagnosis, which provides several advantages over traditional cancer detection methods, including improved accuracy by providing a second opinion to the clinician, faster image interpretation, cost savings, and a reduction in the diversity of pathologists' interpretations. The rapid increase in cancer incidence and mortality rates served as the impetus for this study, which reviewed state-of-the-art machine learning techniques for cancer detection. This research aims to look at the current role of machine learning in cancer detection and diagnosis. We begin this study by providing an overview of machine learning and its various types. Second, this paper mulls over various machine learning methods and datasets that have been used for cancer detection. Third, previous works on cancer detection employing machine learning and deep learning methods are reviewed. Finally, challenges for potential future work are provided to guide the researchers.
An Insight into Machine Learning Techniques for Cancer Detection
Cancer is a dreadful disease and a primary cause of death. Early cancer detection and screening are critical for enhancing survival rates. Conventional cancer diagnostic techniques have a number of disadvantages, including being more expensive, error-prone, and time-consuming. To address this issue, machine learning has been widely used in cancer detection and diagnosis, which provides several advantages over traditional cancer detection methods, including improved accuracy by providing a second opinion to the clinician, faster image interpretation, cost savings, and a reduction in the diversity of pathologists' interpretations. The rapid increase in cancer incidence and mortality rates served as the impetus for this study, which reviewed state-of-the-art machine learning techniques for cancer detection. This research aims to look at the current role of machine learning in cancer detection and diagnosis. We begin this study by providing an overview of machine learning and its various types. Second, this paper mulls over various machine learning methods and datasets that have been used for cancer detection. Third, previous works on cancer detection employing machine learning and deep learning methods are reviewed. Finally, challenges for potential future work are provided to guide the researchers.
An Insight into Machine Learning Techniques for Cancer Detection
J. Inst. Eng. India Ser. B
Chhillar, Indu (Autor:in) / Singh, Ajmer (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 104 ; 963-985
01.08.2023
23 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
An Investigation into the Effectiveness of Machine Learning Techniques for Intrusion Detection
| DOAJ | 2017
| Wiley | 2020
A Novel Insight into Learning Theory: The Gap between Teaching and Learning
| British Library Online Contents | 2007
| DOAJ | 2019
| DOAJ | 2019