RETRACTED ARTICLE: Deep learning techniques for prediction of pneumonia from lung CT images

Abstract

Pneumonia disease is caused by viruses and bacteria which affect one or both lungs. It is the most dangerous disease that causes huge cancer death worldwide. Early detection of Pneumonia is the only way to improve a patient's chance for survival. We can detect this disease from X-ray or Computed Tomography (CT) lung images using Deep Learning Techniques. This research paper provides a solution to medical practitioners in predicting the impact of virus as high-risk, low-risk and medium-risk among the population being tested through various Deep Learning techniques such as Convolutional Neural Networks (CNN), Arti cial Neural Network (ANN) and Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) cells. We observed 3000 CT images of Pneumonia con rmed patients and achieved the accuracy resulting 98-99%. The performance of the classi ers are evaluated using parameters such as confusion matrix, accuracy, F-measure, precision and Recall. The results proves that deep learning afford a tting tool for fast screening of Pneumonia and discovering high-risk patients and preventing them by providing suitable medical remedies.

Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis

“…This systematic review and meta -analysis were conducted to estimate the predictive accuracy and efficiency of artificial intelligence-based deep learning networks and algorithms in the early detection of pneumonia. A total of 13 articles ( Swaminathan et al, 2016 , Smith et al, 2018 , Correa et al, 2018 , Kermany et al, 2018 , Hwang et al, 2019 , Wu et al, 2020 , Hashmi et al, 2021 , Hsu et al, 2022 , Ortiz-Toro et al, 2022 ; Ukwuoma et al, 2022; Ali, 2023 , Meena et al, 2023 , Rajput et al, 2023 ) were included in the meta -analysis. Twelve studies examined the role of deep learning models in the early detection of pneumonia and one study predicted target drug concentrations for the treatment of tuberculosis.…”

“…Overall studies included a total of 126,610 images and 1706 patients in this meta -analysis. Convolutional neural networks ( Smith et al, 2018 , Wu et al, 2020 ; Hashmi et al, 202) and deep learning neural networks ( Kermany et al, 2018 , Hwang et al, 2019 ) were frequently used models, other used AI models included CART ( Swaminathan et al, 2016 ), ANN ( Correa et al, 2018 ), SVM ( Hashmi et al, 2021 , Ortiz-Toro et al, 2022 ), and Hybrid Transformer Encoder (Ukwuoma et al, 2022), Visual Geometry Group 19 ( Ali, 2023 ), Long Short-Term Memory ( Meena et al, 2023 ) Bio-Inspired Optimization Based LSTM ( Rajput et al, 2023 ).…”

“…Another study by Meena et al (2023) employs the LSTM model to predict pneumonia. The study utilized 3000 images of CT scans of the lungs.…”