Nanotechnology in Biomedical Image Processing Using Ensemble Deep Learning Approach

Correct identification of the most recent case of pneumonia fever determines successful therapy and management of the condition. Computable tomography (CT) scans can rapidly and precisely classify and evaluate cases of pneumonia fever. Almost every hospital has chest CT scanning available, which lets one rapidly classify pneumonia fever sufferers. Considering the great availability of chest CT imaging, this seems reasonable. The chest CT-based pneumonia fever categorization requires a lot of time with fast spreading infections. Medical staff workers should be able to gain from automated CT scan analysis considering their busy schedules. In the framework of this work, we build a feature extraction and classification system leveraging CT scan inputs. Part of the framework’s training and testing courses is teaching the classifier—in charge of creating a model that exactly classifies entering CT images. This module among others makes up the framework. The classifier in this work is convolutional neural network (CNN) variants. The performance of the present model is evaluated by the simulation; the results show that the suggested method achieves better accuracy than any other classifications.