Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

Sanjeevaiah, K.; Reddy, Tatireddy Subba; Karthik, S; Kumar, Neeraj; Vivek, D.

doi:10.4018/ijsi.315661

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Reddy et al [40] describe Content-Based Image Retrieval (CBIR), which uses image data attributes rather than labels to scan a library of images for images that are visually similar to a user query. For the purpose of extracting high-level and deep properties from images, the usage of deep learning methodologies in particular Densenet-121-is described.…”

Section: Background Related Workmentioning

confidence: 99%

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Iqbal,

Qureshi,

Alhussein

et al. 2023

IEEE Access

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Medical image retrieval is essential to modern medical treatment because it enables doctors to diagnose and treat a variety of illnesses. In this study, we present an innovative technique for selecting the methodology of medical images by combining textural and visual information. Knowing the imaging process behind an idea, such as a chest X-ray, skin dermatology, or breast histopathology image, may be extremely helpful to healthcare professionals since it can aid in image investigation and provide important information about the imaging technique used. We use deep learning-based feature engineering to do this, using both the textural and visual components of healthcare images. We extract detailed visual information from the images using a predefined Convolutional Neural Network (CNN). The Global-Local Pyramid Pattern (GLPP), Zernike moments, and Haralick are also used to physically separate the pertinent parts from the images' other visual and factual aspects. These essential characteristics, such as image modality and imaging technique-specific characteristics, provide additional information about the technology. We employ a feature fusion method that incorporates the depictions obtained from the two modalities in order to combine the textural and visual elements. This fusion process, which improves the discrimination capacity of the feature vectors, makes accurate modality classification possible. We conducted trials on a sizable dataset consisting of various medical images to assess the effectiveness of our proposed method. The results indicate that, in comparison to conventional methods, our technique outperforms modality retrieval, with a precision of 95.89 and a recall of 96.31. The accuracy and robustness of the classification task are greatly creased by the combination of textural and visual data. Through the integration of textural and visual information, our work offers a unique method for recovering the modality of medical images. This method has the potential to greatly improve the speed and accuracy of medical image processing and diagnosis by helping experts rapidly and accurately identify the imaging technology being utilized.

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Section: Background Related Workmentioning

confidence: 99%

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Iqbal,

Qureshi,

Alhussein

et al. 2023

IEEE Access

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“…The second trend is the increase in the form of a marked shift from cultural and language to data and research data repositories for Open Science using digital scholarship ecosystems. According to Sanjeevaiah, Reddy, Karthik, Kumar, & Vivek (2023), changes in data management center around (CBIR) technology which is content-based image retrieval systems that allow the researcher to search a database to "retrieve the most visually comparable photos to a query image" (Sanjeevaiah et al 2023, p. 1). The authors advise that this technology is pivotal due to the increase in digital images on the internet.…”

Section: Cbirmentioning

confidence: 99%

Identifying and Navigating the Current Trends in Business Librarianship and Data Librarianship

Pistone¹

2023

CIS

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These trends in business librarianship and data librarianship matter for the management of today’s academic libraries and this topic is important to discuss because librarians must respond to the developments in data science and big data. Industry leaders such as Yuanqing Yango, CEO of Lenovo refer to “new IT” and the coming revolution stemming from the usage of smart devices, edge and cloud computing, 5G networks, and (AI) Artificial Intelligence (Lenovo, 2022). Lenovo (2022) researchers undertook a study of 500 Chief Technology Officers (CTOs)from diverse industries to ascertain their perceptions about the future of technology. Both scholars and industry leaders alike agree that the technologies that will dominate will be forged so that humanity can meet the challenges of the future and the control of information will be at the forefront of these changes. Information professionals must learn about and master the technologies that industry leaders are reimagining as innovations that will try to improve our lives because librarianship is becoming increasingly data-driven. Faculty, staff, and students rely on information professionals to help them to understand the role of “new IT” and the opportunities that it creates. We also need more informed professionals because research is data-driven. More decision makers are using big data to make effective organizational decisions. Librarians must be cognizant of the trends that are governing innovations in technology to effectively provide information services to key stakeholders.

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Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models

Gowsic,

et al. 2024

2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)

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Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

Cited by 3 publications

References 29 publications

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Identifying and Navigating the Current Trends in Business Librarianship and Data Librarianship

Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models

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