Prediction Analysis of Urinary Tract Infection through Transfer Learning Techniques

Abstract

The occurrence and presence of a urinary infection in the urinary tract can lead to the damage of normal functioning of the human body including the renal body parts. This may further have a larger impact on the health of an individual and might increase the adversity of his life including the healthcare costs. Hence, its detection at the right stage is important. To achieve the target of this purpose; the proposed research paper aims to focus on detecting the same through the usage of transfer learning models. For this reason the author of the research paper deploys the model in two stages wherein the first stage includes the clinical visit of the patient to the doctor so that manual inspection of the infection can take place and the second stage of the research includes the prediction of the infection through the usage and implementation of transfer learning models and respective algorithms. Four transfer learning based algorithms namely; ResNetV2, MobileNet, Inception and VGGNet are used as transfer learning models and the respective prediction for the presence of urinary infection in the tract is observed.