Felipe Ramos Teixeira
Universidade Federal do Rio de Janeiro (UFRJ), Brazil
Rafael Guilherme Santos
Universidade Federal de Minas Gerais (UFMG), Brazil
ABSTRACT
Pathology and pathologists play an essential role in treating various contagious diseases and analyzing drug expansion and clinical analysis tasks. The workflow of Pathologists is complex, specific and limited by human evaluation. Supportive clinical decision-making tools help transform the pathology field with the help of machine learning tools. It will improve the predictive reads compared to manual reads, automatic approaches for scoring clinical trial slides, discovery of innovative markers and recognizing the pathology of cancer. The machine learning models must require authentication and certification before they are applied for any pathological analysis as they impact individual life and health. Pathological images are immense in resolution and different from the normal image; machine learning models required to solve these problems need an understanding of the biological structure and guidelines to prevent model failure that is supposed to predict life-threatening diseases. Machine learning models like deep learning (DL) models give promising results in detecting the images. Machine learning models help in detecting the histopathological picture of tissues, and the deep learning model is one of the models that shows optimized outputs. Consequently, these images are different from natural images and for analyses of these images, there is a need to demonstrate a machine to recognize them.
Keywords: Machine learning Models (MLM), Predict Epidemiological Trends (PPT), Pathology Data (PD), Smart PLS Algorithm Model.
