ArticleComputational intelligence and neuroscience2022
A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2026Article
- ADSPAO: Enhanced artemisinin optimization for multi-threshold segmentation of chronic obstructive pulmonary disease.iScience · 2026Article
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
- Multi-omics and AI-driven advances in miRNA-mediated hair follicle regulation in cashmere goats.Frontiers in veterinary science · 2025Review
- Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.Histology and histopathology · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.