Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis.
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ABSTRACT:
SUBMITTER: Forte GC
PROVIDER: S-EPMC9405626 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis.
Forte Gabriele C GC Altmayer Stephan S Silva Ricardo F RF Stefani Mariana T MT Libermann Lucas L LL Cavion Cesar C CC Youssef Ali A Forghani Reza R King Jeremy J Mohamed Tan-Lucien TL Andrade Rubens G F RGF Hochhegger Bruno B
Cancers 20220809 16
We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. S ...[more]
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Project description:Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
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