Deploying swarm intelligence in medical imaging identifying metastasis, micro‐calcifications and brain image segmentation

al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, D. Jude

doi:10.1049/iet-syb.2015.0036

Cited by 20 publications

(4 citation statements)

References 21 publications

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“…Moreover, applying swarm intelligence models can potentially simplify the control over these nanorobot swarms, making them more manageable [43]. The application of swarm intelligence extends to medical imaging, where it has been deployed to identify metastasis, micro-calcifications, and brain image segmentation [40], [41], [42]. These findings underscore the potential for nanorobot swarms to revolutionize areas of medicine ranging from targeted therapeutics to sophisticated imaging techniques.…”

Section: Miscellaneousmentioning

confidence: 99%

Nanorobotics in Medicine: A Systematic Review of Advances, Challenges, and Future Prospects with a Focus on Cell Therapy, Invasive Surgery, and Drug Delivery

Rajendran,

Sundararajan,

Awasthi

et al. 2024

Precision Nanomedicine

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Nanorobotics offers an emerging frontier in biomedicine, potentially revolutionizing diagnostic and therapeutic applications through its unique capabilities in manipulating biological systems at the nanoscale. Following PRISMA guidelines, a comprehensive literature search was conducted using IEEE Xplore and PubMed databases, identifying and analyzing 414 papers. The studies were filtered to include only those that addressed nanorobotics and direct medical applications. Our analysis traces the technology’s evolution, highlighting its growing prominence in medicine as evidenced by the increasing number of publications. Applications ranged from targeted drug delivery and single-cell manipulation to minimally invasive surgery and biosensing. Despite the promise, limitations such as biocompatibility, precise control, and ethical concerns were also identified. This review aims to offer a thorough overview of nanorobotics in medicine, specifi-cally on niches such as laparoscopic surgery, drug delivery, and cell manipulation, drawing at-tention to current challenges and opportunities and providing directions for future research in this rapidly advancing field.

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Section: Miscellaneousmentioning

confidence: 99%

Nanorobotics in Medicine: A Systematic Review of Advances, Challenges, and Future Prospects with a Focus on Cell Therapy, Invasive Surgery, and Drug Delivery

Rajendran,

Sundararajan,

Awasthi

et al. 2024

Precision Nanomedicine

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“…Several studies have been conducted by researchers regarding MRI brain tumor segmentation techniques. An brief review of some of the recent studies is presented here; these studies used different methods, including Neural Network Models, Deformable Models, Fuzzy C-Means (FCM), Genetic Algorithms, Level Set Models, Differential Evolutionary Algorithms, Hybrid Clustering, and Artificial Intelligence [19,20].…”

Section: Related Workmentioning

confidence: 99%

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

et al. 2020

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Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

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“…SUI is the most common type of UI, which refers to accidentally passing from the ureteral orifice when the abdominal pressure suddenly rises (sneezing, coughing, laughing, exercising, etc.) [5]. Although SUI does not endanger the life of the patient, frequent urine leakage can cause social dysfunction in women, seriously affect their physiology and psychology, and seriously reduce women's quality of life [6].…”

Section: Introductionmentioning

confidence: 99%

Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence

Zhang

Zhao

et al. 2021

Scientific Programming

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This study aimed to explore the application of pelvic floor ultrasound under particle swarm intelligent optimization algorithm in the preoperative and postoperative evaluation of female stress urinary incontinence (SUI) and provide a theoretical basis for clinical diagnosis. In this study, 90 patients with SUI were enrolled, which were randomly and equally assigned into a blank group (healthy physical examination), control group (perineal ultrasound), and experimental group (pelvic floor ultrasound based on particle swarm intelligence optimization algorithm). The ultrasonic image segmentation and processing were carried out by a particle swarm intelligence optimization algorithm. Patients with stress incontinence were classified as group A, and patients without stress incontinence were classified as group B. The results of previous surgical examinations were the standard to judge the accuracy of pelvic floor ultrasound diagnosis based on the swarm intelligence optimization algorithm. The accuracy of diagnosing stress UI in the experimental group was 90.38%, which was significantly higher than that of the control group (54.31%) and the blank group (38.95%) ( P < 0.05). The formation percentage of the urethral internal orifice in the experimental group was 82.5%, which was significantly higher than that of the control group (65.4%) and the blank group (12.5%), and there was a statistical difference among the groups ( P < 0.05). In the resting state, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was greater than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was smaller than in the blank group, and there were statistical differences among the groups ( P < 0.05). In the state of Valsalva, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was smaller than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was greater than that in group B. The distance of the bladder neck shifting downward was greater than that in group B, and there were statistical differences among the groups ( P < 0.05). In short, the pelvic floor ultrasound based on the particle swarm intelligent optimization algorithm was accurate in the diagnosis of stress UI. The application of pelvic floor ultrasound in the diagnosis of UI provided image data objectively for clinical diagnosis and had a high application value.

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Deploying swarm intelligence in medical imaging identifying metastasis, micro‐calcifications and brain image segmentation

Cited by 20 publications

References 21 publications

Nanorobotics in Medicine: A Systematic Review of Advances, Challenges, and Future Prospects with a Focus on Cell Therapy, Invasive Surgery, and Drug Delivery

Nanorobotics in Medicine: A Systematic Review of Advances, Challenges, and Future Prospects with a Focus on Cell Therapy, Invasive Surgery, and Drug Delivery

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence

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