RETRACTED ARTICLE: Parallel deep convolutional neural network for content based medical image retrieval

Haripriya, P; Porkodi, R

doi:10.1007/s12652-020-02077-w

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…Haripriya and Porkodi [ 24 ] implemented a new parallel deep convolutional neural network (PDCNN) algorithm for an effective CBMIR. The developed algorithm consists of higher level semantic, compact, and lower level content features that significantly handles the imbalanced dataset issues and decreases the DCNN training time in DICOM images.…”

Section: Related Workmentioning

confidence: 99%

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Praveena¹,

Guptha²,

Kazemzadeh

et al. 2022

Journal of Healthcare Engineering

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In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.

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Section: Related Workmentioning

confidence: 99%

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Praveena¹,

Guptha²,

Kazemzadeh

et al. 2022

Journal of Healthcare Engineering

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“…Although our proposed GRHDP shows the superiority in retrieving medical images comparing with other hierarchical diffusion process-based methods, some limitations should be taken into consideration in our next work. Firstly, we only use the basic framework and traditional feature to represent images, which could be further improved, for example, by using state-of-the-art stack auto-encoder [27] or recently proposed parallel convolutional neural network [28]. Then, the initial performance is not satisfactory since the proposed method adopts randomly adaptive way to determine the representative images.…”

Section: Limitation and Discussionmentioning

confidence: 99%

“…Considering this, the content-based retrieval method has attracted increasing attention in image retrieval task. At present, the deep learning-based retrieval methods [25]- [28] have yield promising performance than traditional methods, but they are suffering poor interpretability and complexity analysis. Considering these, manifold preservation and diffusion process are introduced into large-scale images retrieval.…”

Section: Related Workmentioning

confidence: 99%

Graph Regularized Hierarchical Diffusion Process With Relevance Feedback for Medical Image Retrieval

Yao

Zhong

et al. 2021

IEEE Access

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Befitting from the interpretability and the capacity in capturing the underlying manifold structure, diffusion process (DP) has attracted increasing attention in the field of image retrieval. Within it, hierarchical diffusion process (HDP) has achieved satisfactory results in retrieved performance and complexity. However, the existing hierarchical diffusion process methods only diffuse the affinity values in low-level visual space without considering the high-level semantic information, which cause the problem of semantic gap. To overcome these problems, we propose a Graph Regularized Hierarchical Diffusion Process (GRHDP) method with relevance feedback, and apply it to retrieve medical images. The proposed algorithm firstly establishes a hierarchical structure of the images in medical image database and spreads the affinity values among query images and top-layer images by graph regularization diffusion. Then relevance feedback is introduced to adjust the similarity between query images and retrieved images in top layer, and the affinity values are diffused again according to labeled information of feedback. Finally, the similarity between queries and others in database can be obtained by interpolating the diffused results on the top layer from top to bottom. The experimental results show that our proposed GRHDP with relevance feedback has achieved better retrieval performance than manifold ranking and regularized diffusion process (RDP) when returning top retrieved images.

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“…Even the medical data is not very large, where few numbers of layers are needed in the learning, the runtime processing is one of the most challenging issues for data scientists working on medical data. Therefore, the interesting direction of this research is to explore both optimization methods [34] and high-performance computing [35] for boosting the performance of the proposed framework without loss of quality of returned outputs. 3) Case studies.…”

Section: E Case Study On Brain Tumor Segmentationmentioning

confidence: 99%

Secure Collaborative Augmented Reality Framework for Biomedical Informatics

Djenouri

Belhadi

Srivastava

et al. 2022

IEEE J. Biomed. Health Inform.

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Augmented reality is currently a great interest in biomedical health informatics. At the same time, several challenges have been appeared, in particular with the rapid progress of smart sensors technologies, and medical artificial intelligence. This yields the necessity of new needs in biomedical health informatics. Collaborative learning and privacy are some of the challenges of augmented reality technology in biomedical health informatics. This paper introduces a novel secure collaborative augmented reality framework for biomedical health informatics-based applications. Distributed deep learning is first performed across a multi-agent system platform. The privacy strategy is developed for ensuring better communications of the different intelligent agents in the system. In this research work, a system of multiple agents is created for the simulation of the collective behaviours of the smart components of biomedical health informatics. Augmented reality is also incorporated for better visualization of the resulted medical patterns. A novel privacy strategy based on blockchain is investigated for ensuring the confidentiality of the learning process. Experiments are conducted on the real use case of the biomedical segmentation process. Our strong experimental analysis reveals the strength of the proposed framework when directly compared to state-of-theart biomedical health informatics solutions.

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RETRACTED ARTICLE: Parallel deep convolutional neural network for content based medical image retrieval

Cited by 19 publications

References 26 publications

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Graph Regularized Hierarchical Diffusion Process With Relevance Feedback for Medical Image Retrieval

Secure Collaborative Augmented Reality Framework for Biomedical Informatics

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