Implementation features of wounds visual comparison subsystem

Jaworski, Nazariy; Farmaha, Ihor; Marikutsa, Uliana; Farmaha, Taras; Savchyn, Vasyl

doi:10.1109/memstech.2018.8365714

Cited by 6 publications

(3 citation statements)

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“…In the medical field, deep learning has changed the way physicians approach diagnostics [30][31][32], particularly in dermatology and oral health. Photographs of oral lesions, often accompanied by rulers for scale, can be automatically analyzed by algorithms to reduce subjectivity in human assessments [21,22].…”

Section: Ruler Detection In Imagesmentioning

confidence: 99%

“…One of the seemingly simple but critical objects that demand accurate detection is the measurement ruler. Algorithms for ruler detection, recognition, and interpretation are central to the accuracy and efficacy of systems, facilitating automated, high-precision measurements in fields such as aquaculture [10][11][12][13], environmental monitoring [14][15][16][17][18][19][20], medical diagnostics [21][22][23][24][25][26][27][28][29][30][31][32], forensics [33,34], industry [35][36][37][38], museums [39]. However, the task of detecting rulers in images presents challenges that have not been extensively explored in current research (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Matuzevičius

2023

Electronics

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This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems.

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Section: Ruler Detection In Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Matuzevičius

2023

Electronics

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“…There are also improved methods with a slightly more complex formula for calculating the area of wounds with an elliptical form (Jansen, S., Rachel, K. 2016). Importance of this topic is mentioned and proved in papers "Computer software for vulnerometry" (Lukavetskyi, O., et al ,2017) and "Implementation features of wounds visual comparison subsystem" (Jaworski, N. et al, 2018) where, topic from medical point of view and computer program for calculation of wounds area are described. This program adds user an ability to encircle wound on image, using computer mouse, after that, wound area is calculated automatically.…”

Section: Introductionmentioning

confidence: 99%

Wound image segmentation using clustering based algorithms

Farmaha

Banaś

Savchyn

et al. 2019

New Trends in Production Engineering

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Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn't need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.

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The Method of Text Tonality Classification

Shakhovska

2020

2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT)

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Implementation features of wounds visual comparison subsystem

Cited by 6 publications

References 6 publications

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Wound image segmentation using clustering based algorithms

The Method of Text Tonality Classification

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