Data preprocessing in knowledge discovery in breast cancer: systematic mapping study

Chlioui, Imane; Idri, Ali; Abnane, Ibtissam

doi:10.1080/21681163.2020.1730974

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…For our review, we chose the systematic mapping approach because it is established in medical research and provides a well-defined methodology (e.g. [ 44–47 ]). It entails the development of a classification scheme and subsequent classification of relevant literature.…”

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence for omics data: a systematic mapping study

Toussaint,

Leiser,

Thiebes

et al. 2023

Briefings in Bioinformatics

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Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies’ AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.

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Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence for omics data: a systematic mapping study

Toussaint,

Leiser,

Thiebes

et al. 2023

Briefings in Bioinformatics

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“…Preprocessing is a method used to improve image quality and interpretability 39 . Image preprocessing techniques are required to look at mammogram orientation, eliminate noise, and strengthen image quality.…”

Section: Proposed Workmentioning

confidence: 99%

“…Preprocessing is a method used to improve image quality and interpretability. 39 Image preprocessing techniques are required to look at mammogram orientation, eliminate noise, and strengthen image quality. This method is specifically applied to enhance image quality so that the residual and unconnected portions in the mammogram background are removed for further processing.…”

Section: Preprocessingmentioning

confidence: 99%

Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Rajendran¹,

Balasubramaniam²,

Ravi

et al. 2022

Computational Intelligence

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Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer-aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization-crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and

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