PneuTeleCNN: Deep Learning-assisted Framework for Pneumonia Identification in Web-enabled Telemedicine Systems

An important development can be observed in the current environment of rapid technological progress in the healthcare industry, which can be demonstrated by the emergence of telemedicine as a key paradigm. Extensive use of telemedical techniques has happened with a particular focus on Tele-Pneumonia examinations, which provide doctors with crucial resources in remote diagnostic situations. In response to the necessity of accurate pneumonia prediction, we present a novel architecture PneuTeleCNN that makes utilization of pretrained models such as DenseNet-121, Visual Geometry Group (VGG-16), MobileNet, and InceptionV3. This thorough approach of PneuTeleCNN meticulously evaluates how effectively the system detects pneumonia in web-based telemedicine environments, establishing the stage for improved remote healthcare solutions. These model’s performance is rigorously evaluated using key performance indicators, including accuracy, F1-score, recall, and precision. In addition, the evaluation includes sophisticated measures like the confusion matrix and receiver operating characteristic (ROC) curve, which offer an intricate perspective on the model’s performance. To help in the identification of the model that performs better in this scenario, a comparative study is made more more straightforward by presenting of each model’s validation accuracy and validation loss graphs. This PneuTeleCNN framework underscores the commitment to ensuring the robustness and reliability of the proposed telemedical approach for pneumonia detection in remote healthcare scenarios.