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All Journal International Journal of Electrical and Computer Engineering
Thacker, Chintan
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Computer Science & IT Electrical & Electronics Engineering

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A detailed analysis of deep learning-based techniques for automated radiology report generation Dhamanskar, Prajakta; Thacker, Chintan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5906-5915

Abstract

The automated creation of medical reports from images of chest X-rays has the potential to significantly reduce workloads for healthcare providers and accelerate patient care, especially in environments with limited resources. This study provides an extensive overview of deep learning-based techniques designed for radiology report generation from chest X-ray pictures automatically. By examining recent research, we delve into various deep learning architectures and techniques used for this task, including transformer-based approaches, attention mechanisms, sequence-to-sequence models, adversarial training methods, and hybrid models. We also discuss about the datasets used for evaluation and training, as well as future directions and research problems in this area. The significance of deep learning in revolutionizing radiology reporting is further emphasized by our review, which also highlights the need for additional research to address challenges such data accessibility, image quality variability, interpretation of complex findings, and contextual integration. The objective of this research is to present a comparative analysis of cutting-edge methods for developing automated medical report generation to enhance patient outcomes and healthcare delivery.
A comprehensive survey on automatic image captioning-deep learning techniques, datasets and evaluation parameters Chauhan, Harshil; Thacker, Chintan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3257-3266

Abstract

Automatic image captioning is a pivotal intersection of computer vision and natural language processing, aiming to generate descriptive textual content from visual inputs. This comprehensive survey explores the evolution and state-of-the-art advancements in image caption generation, focusing on deep learning techniques, benchmark datasets, and evaluation parameters. We begin by tracing the progression from early approaches to contemporary deep learning methodologies, emphasizing encoder-decoder based models and transformer-based models. We then systematically review the datasets that have been instrumental in training and benchmarking image captioning models, including MSCOCO, Flickr30k, Flickr8k, and PASCAL 1k, discussing image count, types of scenes, and sources. Furthermore, we delve into the evaluation metrics employed to assess model performance, such as bilingual evaluation understudy (BLEU), metric for evaluation of translation with explicit ordering (METEOR), recall-oriented understudy for gisting evaluation (ROUGE), and consensus-based image description evaluation (CIDEr), analyzing their domains, bases, and measurement criteria. Through this survey, we aim to provide a detailed understanding of the current landscape, identify challenges, and propose future research directions in automatic image captioning.
Co-Authors Chauhan, Harshil Dhamanskar, Prajakta