International Journal For Multidisciplinary Research

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Impression Generation From X-ray Images Using Machine Learning

Author(s) Rushikesh Rajan Sawant, Suyog P Sawant, Sarvesh Vijay Karpe, Vaishnav Vijay Kubade, Aditya Vinod Kedari
Country India
Abstract Due to the incorporation of machine learning techniques, medical image analysis has made significant strides in recent years. In this paper, we concentrate on a crucial application: employing machine learning to extract literary impressions from chest X-ray pictures. The goal of the project is to fill the gap between natural language processing and medical imaging by enabling the automatic creation of radio-logical impressions, which are crucial for diagnostic reports. The problem statement includes a number of significant difficulties. A lack of positive examples compared to negative cases in medical datasets can skew model training and impair diagnostic precision. For impression formation, accurate feature extraction from chest X-ray pictures is essential. The
CheXNet model must be carefully adjusted to the particular task at hand in order to be used for this purpose. Converting visual information into cohesive literary perceptions is the main challenge. To do this while minimizing the risk of overfitting, a sequence-to-sequence model with an attention mechanism must be
used to precisely match image attributes with textual information. These difficulties highlight the project’s complexity and highlight the requirement for exact machine learning methods, advanced architectural layouts, and strategic feature extraction in order to enable the automated generation of high-quality
radio-logical images.
Keywords Machine learning, X-ray, Datasets, Models, Accuracy, Precision, Recall, Activation Functions.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-01
Cite This Impression Generation From X-ray Images Using Machine Learning - Rushikesh Rajan Sawant, Suyog P Sawant, Sarvesh Vijay Karpe, Vaishnav Vijay Kubade, Aditya Vinod Kedari - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16076
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16076
Short DOI https://doi.org/gtpw7f
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