International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Automatic radiology report generator using transformer with contrastbased image enhancement R.Lenitha 1, L. Kebila Anns Subi 2, D.Deiva Thaya Sweetlin 3 1PG Student, M.E Communication and networking, Ponjesly college of engineering, Kanyakumari, Tamil Nadu, India. 2Professor M.E Communication and networking, Ponjesly college of engineering, Kanyakumari, Tamil Nadu, India. 3 Assistant Professor M.E Communication and networking, Ponjesly college of engineering, Kanyakumari, Tamil Nadu, India.
---------------------------------------------------------------------------***------------------------------------------------------------------------the necessity for medical professionals. According to Abstract — It takes a lot of time and requires the data published in the Medical Journal of Radiology in 2015, radiologists' workload increased by 26% over the preceding ten years. [1] Due to medical advancements, radiologists now have to compare a lot more information, including many factors, in order to make comprehensive and reliable diagnosis based on medical images. The goal of this project is to create a system that employs cutting-edge natural language processing (NLP) models, specifically transformers, in conjunction with image enhancement techniques to automatically generate radiology reports from medical pictures, such as X-rays, scans, or MRIs. [2]. Its goal is to make the process of creating radiology reports from medical images as efficient as possible. The method improves the visibility of important anatomical features and abnormalities in medical imaging including X-rays, MRIs, and CT scans by employing sophisticated contrast enhancement techniques. Following image processing, a transformer-based natural language model is used to evaluate the improved images' attributes and produce precise, thorough radiologist reports. [3] Sections like as patient information, an evaluation of the image quality, comprehensive findings, and a conclusion with suggestions are usually included in these reports. Structured, context-aware medical reports in natural language can be produced by integrating a transformer model, such GPT or MedBERT. By automating regular report generation, this method not only saves radiologists time but also guarantees full reports, lowering the possibility of oversight. Additionally, in both developed and underprivileged locations, the system could improve accessibility to timely medical interpretations, help healthcare providers, and increase the efficiency of diagnostics. [4] Enhance medical image quality to make pertinent information (such tumors, fractures, or other anomalies) easier to see and identify. To improve image features and highlight important
knowledge of experienced radiologists to write radiology reports based on radiographic pictures. Therefore, it would be beneficial to incorporate technology that can generate reports automatically. The primary difficulty with automatic report generating is creating a logical predictive text. Techniques that can improve the relevance of features in generating predictive text must be developed. This study used the transformer approach and image enhancement technology to build a model for generating medical reports. An methodology to improve the medical image's noiseproneness is investigated in this study, along with the transformers way to produce a radiologist report based on chest X-ray images, in order to take advantage of the visual and semantic elements. The impact of image improvement techniques on the radiology report generator was examined using four contrast-based image enhancement approaches. The encoder-decoder model is employed with a pre-trained model ChexNet and Multi-Head Attention (MHA) mechanism for visual feature extraction and Bidirectional Encoder Representation from Transformer (BERT) for text feature embedding. With a 0.412 value, MHA outperforms the baseline model by 15% as well. This approach can perform better than the baseline model and other earlier studies. It can be said that BERT and the transformer MHA encoder layer work well for utilizing textual and visual information. Furthermore, it has been discovered that incorporating an image enhancement technique improves the model's performance. Key Words: Bidirectional Encoder Representation from Transformer (BERT). Multi-Head Attention (MHA)
1. INTRODUCTION As medical imaging technology advances, medical image diagnostics become increasingly complex, necessitating
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