RETRACTED ARTICLE: Fuzzy-entropic approach on chest X-ray region of interest segmentation-heart position shifting using differential evolution optimization and multi-level segmentation technique with cloud computing

Priya, R. Krishna; Bimani, Ali Al; Bhupathyraaj, Mullaicharam; Ahamed, Suhail; Arputhanantham, Shyam Sundar; Chacko, Susamma

doi:10.1007/s00500-022-07006-x

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…A lightweight CNN approach was devised for multi-class and binary classification of chest X-rays, achieving 98.33% accuracy. Priya, R. Krishna, et.al [13] developed the storage and processing of medical data irrespective of the physical location of lab are explored in this research with Cloud computing. Extracted valid information from a medical image was high importance in the medical arena.…”

Section: Literature Surveymentioning

confidence: 99%

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

Suresh,

Meghana,

Sujitha

et al. 2024

TURCOMAT

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Medical image segmentation plays a crucial role in various clinical applications, facilitating accurate diagnosis and treatment planning. In communication systems, particularly telemedicine and remote healthcare monitoring, real-time processing and transmission of medical images are essential for timely diagnosis and intervention. Leveraging VLSI technology for implementing Kmeans offers a promising solution to address the computational demands of image segmentation while meeting the stringent requirements of communication systems. Existing systems often rely on software-based implementations such as threshold segmentation, leading to significant computational overhead and latency, particularly in resource-constrained environments. Moreover, these implementations struggle to achieve real-time performance, hindering their practical utility in communication systems for healthcare. So, this work proposed VLSI-based approach aims to overcome these challenges by offloading the computational burden from the software to hardware, enabling parallel processing and efficient utilization of resources. By exploiting the inherent parallelism of the K-means algorithm and optimizing hardware architecture, our design ensures high throughput and low latency, making it suitable for real-time medical image segmentation in communication systems.

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Section: Literature Surveymentioning

confidence: 99%

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

Suresh,

Meghana,

Sujitha

et al. 2024

TURCOMAT

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A Segmentation Method Based on PDNet for Chest X-rays with Targets in Different Positions and Directions

Liang

Zhang

et al. 2023

Applied Sciences

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To assess the impact of the relative displacement between machines and subjects, the machine angle and the fine-tuning of the subject posture on the segmentation accuracy of chest X-rays, this paper proposes a Position and Direction Network (PDNet) for chest X-rays with different angles and positions that provides more comprehensive information for cardiac image diagnosis and guided surgery. The implementation of PDnet was as follows: First, the extended database image was sent to a traditional segmentation network for training to prove that the network does not have linear invariant characteristics. Then, we evaluated the performance of the mask in the middle layers of the network and added a weight mask that identifies the position and direction of the object in the middle layer, thus improving the accuracy of segmenting targets at different positions and angles. Finally, the active-shape model (ASM) was used to postprocess the network segmentation results, allowing the model to be effectively applied to 2014 × 2014 or higher definition chest X-rays. The experimental comparison of LinkNet, ResNet, U-Net, and DeepLap networks before and after the improvement shows that its segmentation accuracy (MIoU) are 5%, 6%, 20%, and 13% better. Their differences of losses are 11.24%, 21.96%, 18.53%, and 13.43% and F-scores also show the improved networks are more stable.

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RETRACTED ARTICLE: Fuzzy-entropic approach on chest X-ray region of interest segmentation-heart position shifting using differential evolution optimization and multi-level segmentation technique with cloud computing

Cited by 2 publications

References 18 publications

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

A Segmentation Method Based on PDNet for Chest X-rays with Targets in Different Positions and Directions

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