Impression Generation From X-ray Images Using Machine Learning

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.