A Review on Multiple Approaches to Medical Image Retrieval System

Nair, Lakshmi; Subramaniam, Kamalraj; PrasannaVenkatesan, G. K. D.

doi:10.1007/978-981-15-2780-7_55

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…To exploit the discriminative feature, the proposed architecture is optimized by minimizing the classification; in the dataset, each image comprises a single level. Moreover, the objective is to reduce the loss and can be formulated as shown in (9).…”

Section: Idd-cnn Optimizationmentioning

confidence: 99%

Content-based image retrieval using integrated dual deep convolutional neural network

Mirajkar¹,

Fatima²,

Qadeer³

2023

IJEECS

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The image retrieval focuses on finding images that are similar from a dataset that is of a large scale against an image of a query. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as their shape, colour, and texture. used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deepconvolutional neural networks (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e., learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets of Paris and the oxford dataset considering metrics; also, image retrieval and re-ranking is carried out against the given query. Comparative analysis of various difficulty levels against the different CNN models suggests that IDD-CNN simply outperforms the existing model.

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Section: Idd-cnn Optimizationmentioning

confidence: 99%

Content-based image retrieval using integrated dual deep convolutional neural network

Mirajkar¹,

Fatima²,

Qadeer³

2023

IJEECS

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“…The medical records track is established in TREC [ 158 ]. Medical text retrieval may be seen as a domain-specific text search problem, with the key issue being to cope with the complexity and ambiguity of medical data and queries [ 159 ]. Semantic-based text search approaches are widely used to address the ambiguity problem in medical search with the help of standard terminologies or domain ontologies such as the International Classification of Disease (ICD), Unified Medical Language System (UMLS), and Medical Subject Headings (MeSH).…”

Section: Artificial Intelligencementioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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“…An old research topic in computer vision is content-based image retrieval (CBIR), whose aim is to retrieve similar images in an extensive image gallery by analyzing their visual content (Figure 1). The applications of CBIR include visual geo-localization Arandjelovic et al (2016); Song et al (2016), medical image search Nair et al (2020), person re-identification (Re-ID) Zheng et al (2015), 3D reconstruction Schonberger et al (2015), remote sensing Chaudhuri et al (2019), shopping recommendations in online markets Liu et al (2016), and many others. The primary methods of image retrieval rely on powerful hand-crafted features, such as SIFT 1 , and encoding methods, such as BoW 2 , VLAD 3 , and Fisher Vector.…”

Section: Introductionmentioning

confidence: 99%

A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval

Yousefzadeh¹,

Pourreza²

2023

Preprint

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Content-based image retrieval is the process of retrieving a subset of images from an extensive image gallery based on visual contents, such as color, shape or spatial relations, and texture. In some applications, such as localization, image retrieval is employed as the initial step. In such cases, the accuracy of the top-retrieved images significantly affects the overall system accuracy. The current paper introduces a simple yet efficient image retrieval system with a fewer trainable parameters, which offers acceptable accuracy in top-retrieved images. The proposed method benefits from a dilated residual convolutional neural network with triplet loss. Experimental evaluations show that this model can extract richer information (i.e., high-resolution representations) by enlarging the receptive field, thus improving image retrieval accuracy without increasing the depth or complexity of the model. To enhance the extracted representations' robustness, the current research obtains candidate regions of interest from each feature map and applies Generalized-Mean pooling to the regions. As the choice of triplets in a triplet-based network affects the model training, we employ a triplet online mining method. We test the performance of the proposed method under various configurations on two of the challenging image-retrieval datasets, namely Revisited Paris6k (RPar) and UKBench. The experimental results show an accuracy of 94.54 and Triplet-loss Dilated Residual Network for High-Resolution Representation... 80.23 (mean precision at rank 10) in the RPar medium and hard modes and 3.86 (recall at rank 4) in the UKBench dataset, respectively.

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A Review on Multiple Approaches to Medical Image Retrieval System

Cited by 12 publications

References 14 publications

Content-based image retrieval using integrated dual deep convolutional neural network

Content-based image retrieval using integrated dual deep convolutional neural network

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval

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