Abstract
In this study, we focus on generating readable automatic assisted clinical reports using medical scans/ images. This project is beneficial when it comes to quick analysis of the medical condition and providing on-time treatment. The challenge in this project is the achievement of clinical accuracy and reduce data bias. On the other hand, this endeavor is essential in meeting medical demands in nations with limited resources due to the shortage of radiologists and radiology training programs. Medical picture captioning or report production is the issue that we'll be solving in this case study. Fundamentally, we must use a Convolutional Neural Network (CNN) or a transfer learning algorithm to extract features (bottleneck features) from the photos (preferable as we have less amount of data). Use these traits that were extracted afterward to anticipate the captions. The result would be a string of words resembling a radiologist's report, where a typical report generated by radiologists includes a summary of the findings, a rationale for the examination, and a history of the patient. We were able to create the Python code with this goal in mind. The predictions were analyzed using greedy and beam search techniques. While beam search was shown to create proper phrases, a greedy search was discovered to be significantly faster. In conclusion, a custom final model that uses greedy search is determined to be the best model for this project based on the Bleu score.
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Darapaneni, N. et al. (2023). Redefining the World of Medical Image Processing with AI – Automatic Clinical Report Generation to Support Doctors. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_65
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