A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method

Zhang, Yanzhu; Yang, Lijun; Li, Yan

doi:10.3390/fractalfract6100579

Cited by 9 publications

(4 citation statements)

References 40 publications

(80 reference statements)

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“…Research in fractional differential equations spans across multiple disciplines and finds applications in a wide range of fields. These include continuum mechanics fluid mechanics, control systems, circuit systems, heat transfer, elasticity, electric drives, signal analysis, quantum mechanics, biomathematics, biomedicine, and social systems, bioengineering [37,38].…”

Section: An Overview Of the Modified Frpdamentioning

confidence: 99%

An Accurate Disease Classification of COVID-19 and Pneumonia from Chest X-Ray Images Utilizing Mathematical Algorithm-Based Deep Learning Model

Alghanmi

2024

MMEP

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The COVID-19 pandemic, which originated in 2019, has caused a significant global number of fatalities. The economic and healthcare impacts of COVID-19 infection in survivors have become evident during this period. An important first step towards the effective management of COVID-19 is an effective screening of patients, which includes radiology examinations using chest radiography as one of the primary screening modalities. Early research has shown that patients with pneumonia and COVID-19 infection show different anomalies in chest radiography images. Classifying images of COVID-19 and pneumonia diseases has proven to be a challenging task for computers. Several classification systems were developed using different databases in order to determine the category to which the detected image belongs. The accuracy percentage was assessed using these systems. However, there are instances where the imaging techniques may produce distorted images, low contrast images, or fail to accurately depict the edges of the internal organs. These challenges can have an impact on the accuracy of a classification model's design. In this study, a new robust model called FPD-VGG-16 is introduced. This model combines the Visual Geometry Group (VGG-16) deep learning technique with the Fractional Partial Differential (FrPDA) mathematical method. The idea of using FrPDA is to improve edges, increase the visibility of texture details, and retain smooth areas in comparison to using only deep algorithms. The proposed model demonstrates accurate pneumonia detection and COVID-19 classification from chest X-ray images; the model recorded an impressive accuracy of 98.1%, along with equally remarkable precision and recall values 0.982 and 0.980 respectively, as well as and f1-score score of 0.981. While 96.2% as an accuracy measure is achieved in this study without using the FrPDA algorithm.

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Section: An Overview Of the Modified Frpdamentioning

confidence: 99%

An Accurate Disease Classification of COVID-19 and Pneumonia from Chest X-Ray Images Utilizing Mathematical Algorithm-Based Deep Learning Model

Alghanmi

2024

MMEP

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“…The region-based model is robust to noise and initial contour position and effectively handles images with weak edges, allowing for accurate and reliable segmentation results. However, it faces challenges with images containing non-uniform gray levels in the target or background areas [13].…”

Section: Active Contour Modelmentioning

confidence: 99%

Improvement of Tiny Object Segmentation Accuracy in Aerial Images for Asphalt Pavement Pothole Detection

Kim

Seo

Jeon

2023

Sensors

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In this study, we propose an algorithm to improve the accuracy of tiny object segmentation for precise pothole detection on asphalt pavements. The approach comprises a three-step process: MOED, VAPOR, and Exception Processing, designed to extract pothole edges, validate the results, and manage detected abnormalities. The proposed algorithm addresses the limitations of previous methods and offers several advantages, including wider coverage. We experimentally evaluated the performance of the proposed algorithm by filming roads in various regions of South Korea using a UAV at high altitudes of 30–70 m. The results show that our algorithm outperforms previous methods in terms of instance segmentation performance for small objects such as potholes. Our study offers a practical and efficient solution for pothole detection and contributes to road safety maintenance and monitoring.

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“…Image processing has been widely adopted in various fields [3][4][5][6][7][8][9][10][11]. In addition, recently, deep learning methods have offered various solutions, and the use of computer vision has grown significantly in various applications including building monitoring, image enhancement, medical image processing, biomedical engineering, and underwater computer vision, where some research has adopted fractal-related perspectives, also in [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

Akram,

Hong,

Kim

et al. 2024

Fractal Fract

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The segmentation of crops and weeds from camera-captured images is a demanding research area for advancing agricultural and smart farming systems. Previously, the segmentation of crops and weeds was conducted within a homogeneous data environment where training and testing data were from the same database. However, in the real-world application of advancing agricultural and smart farming systems, it is often the case of a heterogeneous data environment where a system trained with one database should be used for testing with a different database without additional training. This study pioneers the use of heterogeneous data for crop and weed segmentation, addressing the issue of degraded accuracy. Through adjusting the mean and standard deviation, we minimize the variability in pixel value and contrast, enhancing segmentation robustness. Unlike previous methods relying on extensive training data, our approach achieves real-world applicability with just one training sample for deep learning-based semantic segmentation. Moreover, we seamlessly integrated a method for estimating fractal dimensions into our system, incorporating it as an end-to-end task to provide important information on the distributional characteristics of crops and weeds. We evaluated our framework using the BoniRob dataset and the CWFID. When trained with the BoniRob dataset and tested with the CWFID, we obtained a mean intersection of union (mIoU) of 62% and an F1-score of 75.2%. Furthermore, when trained with the CWFID and tested with the BoniRob dataset, we obtained an mIoU of 63.7% and an F1-score of 74.3%. We confirmed that these values are higher than those obtained by state-of-the-art methods.

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A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method

Cited by 9 publications

References 40 publications

An Accurate Disease Classification of COVID-19 and Pneumonia from Chest X-Ray Images Utilizing Mathematical Algorithm-Based Deep Learning Model

An Accurate Disease Classification of COVID-19 and Pneumonia from Chest X-Ray Images Utilizing Mathematical Algorithm-Based Deep Learning Model

Improvement of Tiny Object Segmentation Accuracy in Aerial Images for Asphalt Pavement Pothole Detection

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

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