Medical Image Captioning Using Optimized Deep Learning Model

Singh, Arjun; Raguru, Jaya Krishna; Prasad, G. Durga; Chauhan, Surbhi; Tiwari, Pradeep Kumar; Zaguia, Atef; Ullah, Mohammad Aman

doi:10.1155/2022/9638438

Cited by 15 publications

(5 citation statements)

References 49 publications

(47 reference statements)

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“…A revolutionary show attends and tells (ATM) paradigm has been created by Singh et al (2022) and put into practice. There has been developed an encoder-decoder structure-based visual attention method.…”

Section: Related Workmentioning

confidence: 99%

Automated Medical Image Captioning with Soft Attention-Based LSTM Model Utilizing YOLOv4 Algorithm

Ravinder,

Srinivasan

2024

Journal of Computer Science

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Section: Related Workmentioning

confidence: 99%

Automated Medical Image Captioning with Soft Attention-Based LSTM Model Utilizing YOLOv4 Algorithm

Ravinder,

Srinivasan

2024

Journal of Computer Science

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“…In this paper [8] author introduce visual attention mechanism based on the encoder-decoder structure, a novel show, attend, and tell model has been designed and implemented. In this paper, SPEA-II has been used to tune the initial attributes of an SPEA-II-based.…”

Section: Literature Surveymentioning

confidence: 99%

Medical Image Captioning Using Deep Learning

2023

IRJMETS

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Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights isa routine, yet Medical image captioning is an emerging field that involves generating textual descriptions for medical images using deep learning techniques. This review paper provides a comprehensive analysis of the state-of-the-art techniques in medical image captioning using deep learning. The paper discusses different deep learning models, evaluation metrics, challenges, and future directions in this field. Nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process.Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases;(ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports.

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“…Fig. 1 demonstrates a typical deeplearning-based approach for image-to-text description [5].Concerning the encoder-decoder image captioning approaches, Convolutional Neural Networks (CNNs) have been exploited as encoders for visual feature extraction from the images, and Recurrent Neural Networks (RNNs), "especially LSTM (Long Short-Term Memory) networks" have been exploited as decoders for transforming the obtained features into various natural languages [6,7]. However, encoder-decoder-based approaches are not capable of analyzing the images over time and considering the spatial prospects of images that are pertinent to the image description (alternatively, creating descriptions for the entire scene).…”

Section: Introductionmentioning

confidence: 99%

Image to Text Description Approach based on Deep Learning Models

Hameed Arif

2024

BAJEST

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The image-to-text description can be indicated by creating captions for images that comply with human language perception. Nowadays, with the speedy progress of deep learning models, image-to-text description (or image captioning) has an expanding consideration by numerous researchers in diverse artificial intelligence relevant applications. In general, accurately getting the semantic information of the principal objects in the images and captioning the association among them represents a crucial issue in this field. In this paper, an image-to-text description approach based on Inception-ResNetV2-LSTM with an attention technique is proposed for effective textual descriptions of images. In this proposed approach, Inception-ResNetV2 is exploited to extract essential features, and the integration of LSTM with the attention technique is implemented as a sentence-creation model in such a way that the learning could be concentrated on specific portions within the images, hence enhancing the performance of image-to-text description approach. In terms of the Meteor and BLEU (1-4) measurements, the proposed approach outperformed other state-of-the-art approaches with 0.787 and (0.977, 0.964, 0.886, and 0.759), respectively

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Medical Image Captioning Using Optimized Deep Learning Model

Cited by 15 publications

References 49 publications

Automated Medical Image Captioning with Soft Attention-Based LSTM Model Utilizing YOLOv4 Algorithm

Automated Medical Image Captioning with Soft Attention-Based LSTM Model Utilizing YOLOv4 Algorithm

Medical Image Captioning Using Deep Learning

Image to Text Description Approach based on Deep Learning Models

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