Insight into machine learning techniques for cancer detection
By: Chhillar, Indu
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Contributor(s): Singh, Ajmer.
Publisher: USA Springer 2023Edition: Vol.104(4), Aug.Description: 963-985p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: 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.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0322 |
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.
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