Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures. (November 2021)

• Record Type: Journal Article
• Authors: Manickam, Adhiyaman; Jiang, Jianmin; Zhou, Yu; Sagar, Abhinav; Soundrapandiyan, Rajkumar; Dinesh Jackson Samuel, R.
• Is Part Of: Measurement. Volume 184(2021)
• Publication Date: 2021-11
• Subjects: Pneumonia Detection -- Deep Learning -- Transfer Learning -- ResNet 50 -- Inception V3 -- Adam Optimizer -- Stochastic Gradient Descent Optimizer -- U-Net -- Convolutional Neural Networks -- Accuracy; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109953
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A deep learning method is proposed using different optimizers and transfer learning to classify Pneumonia patients. Preparing a dataset of around 5300 X-ray images for pneumonia detection. The proposed deep transfer learning method is trained on a benchmark open dataset of chest x-ray images. Presenting the optimization results, precision, recall, accuracy, and F1-score for proposed method. In proposed method achieve better accuracy in the detection rate than other techniques. Abstract: Pneumonia is a disease that leads to the death of individuals within a short period since the flow of fluid in the lungs. Hence, initial diagnosis and drugs are very important to avoid the progress of the disease. This paper proposes a novel deep learning approach for automatic detection of pneumonia using deep transfer learning to simplify the detection process with improved accuracy. This work was aimed to preprocess the input chest X-ray images to identify the presence of pneumonia using U-Net architecture based segmentation and classifies the pneumonia as normal and abnormal (Bacteria, viral) using pre-trained on ImageNet dataset models such as ResNet50, InceptionV3, InceptionResNetV2. Besides, to extract the efficient features and improve accuracy of pre-trained models two optimizers, namely, Adam and Stochastic Gradient Descent (SGD) used and its performances are analyzed with batch sizes of 16 and 32. Based on the values obtained, the performances of undertaken pre-trained models are analyzed and compared with other Convolutional Neural Network (CNN) models such as DenseNet-169+SVM, VGG16, RetinaNet + Mask RCNN, VGG16 and Xception, Fully connected RCNN, etc using various measures. From the results observed that the proposed ResNet50 model work achieved 93.06% accuracy, 88.97 % precision rate, 96.78% Recall rate and 92.71% F1-score rate, which than is higher than the other models aforementioned.