Survey Paper on Analysis of Microscopic Medical Images Using Deep Learning

Abstract

In the field of PAM (Photo acoustic Microscopy), researchers have addressed the issue of achieving a balance between spatial conclusions and imaging celerity. They employed deep literacy principles to reconstruct under-tried PAM images, resulting in significant advancements. Their approach involved the development of a fully thick U-net (FDU-net) model, utilizing various convolutional neural network (CNN) structures. The FDU-net model exhibited robustness, which was validated through simulations under real-world conditions. This allowed for the testing and training of the deep literacy model using non-identical imaging portions. Numerical analysis and results provided substantial evidence of the model's effectiveness in reconstructing PAM images, even with only 2 original pixels.

The incorporation of deep literacy principles in the FDU-net model has the potential to reduce imaging time while preserving image quality in PAM. This approach offers a promising solution to the significant challenge of balancing spatial conclusion and imaging celerity in PAM. The researchers demonstrated the practicality and efficacy of their methodology, emphasizing the positive outcomes achieved through the application of deep learning techniques.

Overall, this research presents a noteworthy advancement in the field of PAM by addressing the need for improved spatial conclusion and imaging celerity. The successful implementation of the FDU-net model and deep literacy principles provides a promising path for further advancements in PAM imaging techniques.