Text Summarization and Image Decomposition of Medical Documents

Nowadays the amount of data being generated in various fields, including the medical domain, has reached unprecedented levels. Consequently, the storage requirements have also increased significantly. Furthermore, accessing relevant information in emergency situations becomes a daunting task amidst such vast volumes of data. This challenge is particularly critical in medical field where a substantial amount of data is derived from scanning reports, encompassing both textual and image data.In this context, we present a methodology aimed at addressing the storage needs of medical imaging outputs in a more efficient manner. Our proposed approach not only reduces the storage requirements but also ensures that the data is stored in a compact format, making it readily available for emergency situations. The efficacy of this methodology has been demonstrated through a series of experiments, yielding promising results.