Pre-processing and feature extraction

Torres-García, Alejandro A.; Mendoza-Montoya, Omar; Molinas, Marta; Antelis, Javier M.; Moctezuma, Luis Alfredo; Hernández-Del-Toro, Tonatiuh

doi:10.1016/b978-0-12-820125-1.00014-2

Cited by 18 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Filtering is a technique used to remove noise and artifacts from the images, such as Gaussian noise, speckle noise, or motion blur. It can be done using different methods, such as average filter, median filter, adaptive median filter, or Gaussian filter ( 70 , 71 ). Normalization technique can adjust the intensity values of the images to a common scale, such as 0-1 or 0-255.…”

Section: Discussionmentioning

confidence: 99%

“…Principal Component analysis (PCA), max pooling, t-distributed stochastic neighbor embedding (t-SNE) and Test-time augmentations (TTA) techniques are widely used feature extraction techniques in the literature. Principal component analysis (PCA can reduces the dimensionality of the data by projecting it onto a lower-dimensional subspace that captures most of the variance ( 71 ). Max pooling reduces the size of the feature maps by applying a max operation over a sliding window and it can helps to extract the most salient features and make them invariant to small translations ( 71 ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Taddese,

Tilahun,

Awoke

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

IntroductionGynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.MethodsThe study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.ResultsThe review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias.ConclusionThis review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Taddese,

Tilahun,

Awoke

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…In another framework that is infrequently considered, given the flexibility of AI, the raw outputs may be utilized directly as inputs of deep learning models such as autoencoders and convolutional neural networks as well as traditional feature extraction methods to allow the algorithms to use features (or predictors) extracted either automatically or manually from the unstructured data for subsequent analysis. (Navamani, 2019; Song et al, 2020; Torres‐García et al, 2022; Vani Kumari & Usha Rani, 2020; Zlotogorski‐Hurvitz et al, 2019). Depending on the choice of feature extraction to be performed, the availability of outcome labels, and the need for exploratory analysis, the selection of optimal biomarkers and operationalizing the biomarker platform may proceed as a supervised, semi‐supervised, self‐supervised, or unsupervised learning task.…”

Section: Implementing Ai‐assisted Saliva Liquid Biopsy For Oral and M...mentioning

confidence: 99%

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Adeoye

2023

Oral Diseases

View full text Add to dashboard Cite

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease‐related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi‐omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

show abstract

“…Classification algorithms use labeled data to learn how to categorize the signals. Classification is a technique for identifying patterns in the data that correspond to different states or conditions [61].…”

Section: Hyungui Et Al Used the Temporal Dependency Of The Extracted ...mentioning

confidence: 99%

“…The model's performance was assessed in terms of accuracy, precision, recall, and F1-score, as defined by equations ( 1), ( 2), (3), and (4), respectively. The importance of carefully selecting features for classification and assessing performance was stressed in [61]. Techniques like LDA, SVM, and ANNs were discussed for classification in signals.…”

Section: Accuracy =mentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Abubakar,

OTHMANI,

Sabri

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.</p>

show abstract

Pre-processing and feature extraction

Cited by 18 publications

References 35 publications

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Contact Info

Product

Resources

About