Text-Based Image Retrieval Using Deep Learning

Singhania, Udit; Tripathy, B. K.

doi:10.4018/978-1-7998-3479-3.ch007

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…At its most basic level, a CNN may be thought of as an ANN that computes the total input in at least one of the layers using convolution rather than the usual ANN method of matrix multiplication. It has been successfully used to solve a number of real-world issues, such as text-based picture retrieval [4], audio signal classification, investigation of gene traits [5], computational biology [6], segmenting MRI of the brain [7], diabetic retinopathy [8], COVID-19 detection [9], and many others.…”

Section: Mri (Magnetic Resonance Imagingmentioning

confidence: 99%

A Novel Approach to Identify the Brain Tumour Using Convolutional Neural Network

Khari,

Gupta,

Chaudhary

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Determining the possibility that an individual is affected by a tumour is an intricate process in today's modern technological and biological age, when feats are reaching unprecedented levels with every passing second. Machine learning modalities could dramatically enhance the accuracy of diagnosis. OBJECTIVES: Our research makes it feasible to detect tumours early, aiding in early diagnosis, and is a necessity for the curative efforts of cancer patients. METHODS: In our research model Convolutional Neural Network (CNN) was implemented using Jupiter to give an accurate result. RESULTS: In our proposed model we got 99% accuracy that is higher than the other results. CONCLUSION: Our research demonstrates the potential of using machine learning techniques to improve the accuracy and efficiency of medical diagnosis.

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Section: Mri (Magnetic Resonance Imagingmentioning

confidence: 99%

A Novel Approach to Identify the Brain Tumour Using Convolutional Neural Network

Khari,

Gupta,

Chaudhary

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…Text-based image retrieval (TBIR) [1], [2] and contentbased image retrieval (CBIR) [3], [4] are dominant paradigms in the field of image retrieval. TBIR facilitates the identification of images relevant to a given natural language query by utilizing the query itself.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions

Yang,

Ismail,

Pang

et al. 2024

IEEE Access

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Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review.INDEX TERMS Sketch-based image retrieval, SBIR, SLR, PRISMA, deep learning.

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