Predicting Uterine Cancer Outcomes using Deep Learning on Genomic Diagnosis from Histopathology Images
A. Kavitha, a b, R Divislay · 2024
Uterine cancer is still a significant health problem among females around the world. Early diagnosis and correct disease prognosis are essential to applying effective treatment., A new approach is proposed for outcome prediction in uterine cancer based on deep learning methods in the analysis of genomic data and machine learning models of histopathology image analysis. The methodology relates genomic data to histopathology images, building up a better diagnostic tool. The introduced deep learning framework for analyzing genomic profiles, especially biomarkers that present a prognosis for uterine cancer. The developed machine learning-based models to classify histopathology images obtained from uterine tissues, thus increasing the accuracy of diagnosis. combine these two data sources using a new fusion technique; this further raises the bar in the performance of this prediction by making a more accurate representation of changes occurring in cancer. Preliminary results indicate a significant increase in the accuracy of classification and outcome prediction, outperforming the existing methods. This integrated approach will provide a sound framework for early detection and personalized treatment strategies for uterine cancer by responding to the current limitations of diagnostic methodologies and offering new insights into the disease management process. algorithm integrates genomics and histopathological data to provide positive outcomes with the prediction of uterine cancer. The data collected is preprocessed, and then applied to two separate models which serve as critical feature extractors in the form of a CNN for image data and Transformer for genomic data, whose outputs are fused with an attention mechanism to bring out informative features that augment better predictive accuracy. It undergrows robust validation incorporating performance metrics, and will be released for clinical use in real-life applications with continuous monitoring to ensure adaptability as well as accuracy.