Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

Rezaie, Afsaneh Abdollahzadeh; Habiboghli, Ali

doi:10.9781/ijimai.2017.452

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Sairam et al [11] introduced an edge detection method based on Canny edge detection for lung tumor detection. Rezaie et al [12] first selected regions of interest containing suspicious nodules using a thresholding method, followed by nodule localization using edge detection algorithms. These methods encompass morphological operations, edge detection, multi-thresholding techniques, and their combinations.…”

Section: The Lung Cancer Detection Methods Based On Manually Set Feat...mentioning

confidence: 99%

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

Wang,

Zhang,

Zhang

et al. 2024

Bioengineering

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When dealing with small targets in lung cancer detection, the YOLO V8 algorithm may encounter false positives and misses. To address this issue, this study proposes an enhanced YOLO V8 detection model. The model integrates a large separable kernel attention mechanism into the C2f module to expand the information retrieval range, strengthens the extraction of lung cancer features in the Backbone section, and achieves effective interaction between multi-scale features in the Neck section, thereby enhancing feature representation and robustness. Additionally, depth-wise convolution and Coordinate Attention mechanisms are embedded in the Fast Spatial Pyramid Pooling module to reduce feature loss and improve detection accuracy. This study introduces a Minimum Point Distance-based IOU loss to enhance correlation between predicted and ground truth bounding boxes, improving adaptability and accuracy in small target detection. Experimental validation demonstrates that the improved network outperforms other mainstream detection networks in terms of average precision values and surpasses other classification networks in terms of accuracy. These findings validate the outstanding performance of the enhanced model in the localization and recognition aspects of lung cancer auxiliary diagnosis.

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Section: The Lung Cancer Detection Methods Based On Manually Set Feat...mentioning

confidence: 99%

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

Wang,

Zhang,

Zhang

et al. 2024

Bioengineering

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“…We will employ the box-counting approach in our research since the box-counting dimension [14] is a mathematical design, and an estimate of the box-counting dimension is simple. This value has become one of the most commonly used generic dimensions [39,40]. We used MATLAB version r2022b to do the FD assessments and analyses.…”

Section: Calculation Of Fd and Imagementioning

confidence: 99%

Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Ghaderi

2023

Journal of Nanomaterials

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One of the most fundamental subjects in nanoscience and nanotechnology is structural analysis. We employed a scanning electron microscope (SEM) image of the manufactured Gd3+/13X/DOX/FA nanocomposite in this study. The size, dimensions, and morphology of nanocomposite materials were studied to ensure the uniformity and homogeneity of SEM images. This is the first study to look at segmented SEM images for fractal dimension (FD) and other statistical criteria, including average, maximum, minimum, skewness, and range for magnetic resonance imaging (MRI) nanocomposite. The average of FD (FDavg), the standard deviation of FD (FDsd), and the lacunarity of FD (FDlac) fractal data analysis criteria were also employed. The findings show that particle sizes and shapes vary because the minimum-to-maximum range is not zero, and our data provide a reasonable range. This interpretation is further supported by an analysis of the nanocomposite’s SEM image. At first glance, the image seemed to be uniform. However, when the calculations were performed, it was discovered that the generated particles were not particularly uniform. The particles were uniformly dispersed throughout all surfaces, although their sizes, dimensions, and morphologies varied. In conclusion, the study was supported by fractal data analysis, emphasizing the importance of structural analysis for future research, particularly for medical applications like MRI.

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“…The computer is able to make timely updates according to the preferences of the audience, providing works corresponding to the needs of the audience. Of course, this interaction can also distract the audience's concentration [10][11].The more varied images of VR can especially disturb the audience's concentration and therefore can lead to a larger number of audiences losing interest in the director's shots.It is the result of intoxicated virtual world instead of senseless stories story. In the process of producing multimedia images, in order to overcome this series of drawbacks, the director uses long shots for correct stage deviation; stereo backgrounds, enhanced lighting and special effects in the form of attracting the audience's focus and making the director's pre-determined work unfold through perspective and creativity.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection and Simulation Analysis of Multimedia Images Based on Intelligent Monitoring Robot

Wang,

2024

IJCAI

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Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

Cited by 6 publications

References 18 publications

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Edge Detection and Simulation Analysis of Multimedia Images Based on Intelligent Monitoring Robot

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