Cardiac image segmentation using Simulated Genetic algorithm

Singh, Vijai; Misra, A. K.; Varsha,

doi:10.1109/icacea.2015.7164857

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…In [29], the authors segmented the images of moving objects (cardiac image) in image series. They used a simulated GA that applies K-means clustering in the two-dimensional feature vector space.…”

Section: Image Segmentation Based On Genetic Algorithms (Ga)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

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Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.

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Section: Image Segmentation Based On Genetic Algorithms (Ga)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

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“…To this end, protecting plants from diseases is of paramount importance. It is one of the major factors that help reduce waste in agricultural production [2]. Although popular and easy, traditional methods of detecting diseases by naked eye observations are insufficient to determine the disease [3].…”

Section: Introductionmentioning

confidence: 99%

On Using Deep Artificial Intelligence to Automatically Detect Apple Diseases from Leaf Images

2022

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Plant diseases, if misidentified or ignored, can drastically reduce production levels and harvest quality. Technology in the form of artificial intelligence applications has the potential to facilitate and improve the disease identification process, which in turn will empower prompt control. More specifically, the work in this paper addressed the identification of three common apple leaf diseases—rust, scab, and black rot. Twelve deep transfer learning artificial intelligence models were customized, trained, and tested with the goal of categorizing leaf images into one of the aforementioned three diseases or a healthy state. A dataset of 3171 leaf images (621 black rot, 275 rust, 630 scab, and 1645 healthy) was used. Extensive performance evaluation revealed the excellent ability of the transfer learning models to achieve high values (i.e., >99%) for F1 score, precision, recall, specificity, and accuracy. Hence, it is possible to design smartphone applications that enable farmers with poor knowledge or limited access to professional care to easily identify suspected infected plants.

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Application of Genetic Algorithm (GA) in Medical Science: A Review

Karmakar

2021

Second International Conference on Sustainable Technologies for Computational Intelligence

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Cardiac image segmentation using Simulated Genetic algorithm

Cited by 4 publications

References 11 publications

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

On Using Deep Artificial Intelligence to Automatically Detect Apple Diseases from Leaf Images

Application of Genetic Algorithm (GA) in Medical Science: A Review

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