Supervised Deep Learning Techniques for Image Description: A Systematic Review

López-Sánchez, Marco; Hernández-Ocaña, Betania; Chávez-Bosquez, Oscar; Hernández-Torruco, José

doi:10.3390/e25040553

Cited by 6 publications

(4 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, it should be acknowledged that other supervised machine learning methods not only exist but may also surpass decision treebased approaches in certain situations. As mentioned earlier, probably a prime example would be the versatile family artificial neural networks encompassing multilayer perceptrons and convolutional neural networks which are especially adept in computer vision applications [36][37][38] . The second limitation of our study concerns the relatively limited size of the sample used for training and testing the random forest and gradient boosting models.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

Pantic,

Vucevic,

Radosavljevic

et al. 2024

Preprint

View full text Add to dashboard Cite

This study focuses on the development of machine learning models based on the features of the run length matrix (RLM) and wavelet analyses, with the potential to detect subtle alterations in hepatocyte chromatin organization due to iron oxide nanoparticle exposure. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

Pantic,

Vucevic,

Radosavljevic

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Inspired by the U-Net [64], AutoST-Net combines encoder-decoder [65] architecture with an attention mechanism. The detailed architecture is shown in Figure 3.…”

Section: Autost-netmentioning

confidence: 99%

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

Chen,

Tian,

Zheng

et al. 2024

Forests

View full text Add to dashboard Cite

Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of forest fire spread prediction are constrained by their ability to process multidimensional fire-related data, particularly in the integration of spatiotemporal information. To address these limitations and enhance the accuracy of forest fire spread prediction, we proposed the AutoST-Net model. This innovative encoder–decoder architecture combines a three-dimensional Convolutional Neural Network (3DCNN) with a transformer to effectively capture the dynamic local and global spatiotemporal features of forest fire spread. The model also features a specially designed attention mechanism that works to increase predictive precision. Additionally, to effectively guide the firefighting work in the southwestern forest regions of China, we constructed a forest fire spread dataset, including forest fire status, weather conditions, terrain features, and vegetation status based on Google Earth Engine (GEE) and Himawari-8 satellite. On this dataset, compared to the CNN-LSTM combined model, AutoST-Net exhibits performance improvements of 5.06% in MIou and 6.29% in F1-score. These results demonstrate the superior performance of AutoST-Net in the task of forest fire spread prediction from remote sensing images.

show abstract

“…Image captioning [12] is a computer-vision task that generates natural language descriptions for images. Deep-learning techniques, including encoder-decoder architectures and attention mechanisms, have been employed for this purpose.…”

Section: Overview Of Computer Vision and Image-processing Techniques ...mentioning

confidence: 99%

Transforming Healthcare: Leveraging Vision-Based Neural Networks for Smart Home Patient Monitoring

Gibet Tani,

Eloutouate,

Elouaai

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Image captioning is a promising technique for remote monitoring of patient behavior, enabling healthcare providers to identify changes in patient routines and conditions. In this study, we explore the use of transformer neural networks for image caption generation from surveillance camera footage, captured at regular intervals of one minute. Our goal is to develop and evaluate a transformer neural network model, trained and tested on the COCO (common objects in context) dataset, for generating captions that describe patient behavior. Furthermore, we will compare our proposed approach with a traditional convolutional neural network (CNN) method to highlight the prominence of our proposed approach. Our findings demonstrate the potential of transformer neural networks in generating natural language descriptions of patient behavior, which can provide valuable insights for healthcare providers. The use of such technology can allow for more efficient monitoring of patients, enabling timely interventions when necessary. Moreover, our study highlights the potential of transformer neural networks in identifying patterns and trends in patient behavior over time, which can aid in developing personalized healthcare plans.

show abstract

Supervised Deep Learning Techniques for Image Description: A Systematic Review

Cited by 6 publications

References 78 publications

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

Transforming Healthcare: Leveraging Vision-Based Neural Networks for Smart Home Patient Monitoring

Contact Info

Product

Resources

About