Pneumonia Transfer Learning Deep Learning Model from Segmented X-rays

Abstract

Pneumonia is a common disease that occurs in many countries, more specifically, in poor countries. This disease is an obstructive pneumonia which has the same impression on pulmonary radiographs as other pulmonary diseases, which makes it hard to distinguish even for medical radiologists. Lately, image processing and deep learning models are established to rapidly and precisely diagnose pneumonia disease. In this research, we have predicted pneumonia diseases dependably from the X-ray images, employing image segmentation and machine learning models. A public labelled database is utilized with 4000 pneumonia disease X-rays and 4000 healthy X-rays. ImgNet and SqueezeNet are utilized for transfer learning from their previous computed weights. The proposed deep learning models are trained for classifying pneumonia and non-pneumonia cases. The following processes are presented in this paper: X-ray segmentation utilizing BoxENet architecture, X-ray classification utilizing the segmented chest images. We propose the improved BoxENet model by incorporating transfer learning from both ImgNet and SqueezeNet using a majority fusion model. Performance metrics such as accuracy, specificity, sensitivity and Dice are evaluated. The proposed Improved BoxENet model outperforms the other models in binary and multi-classification models. Additionally, the Improved BoxENet has higher speed compared to other models in both training and classification.

Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model

“…[64] The authors introduce a transfer learning method using channel attention deep convolutional neural network architectures for accurate pediatric pneumonia diagnosis, demonstrating its potential in improving diagnostic accuracy in pediatric healthcare settings by detecting pneumonia in chest X-ray images. [65] The study explores a deep learning model that uses transfer learning techniques for segmented X-ray images, demonstrating improved performance and potential for accurate pneumonia diagnosis, providing valuable insights for the advancement of diagnostic tools in the healthcare industry. [66] This research aims to develop a deep attention network for analyzing chest X-ray images for pneumonia diagnosis.…”

Real-time pneumonia prediction using pipelined spark and high-performance computing

“…Implementing different Deep Learning algorithms on massive data may be easier with Big Data tools, especially when dealing with very complicated medical datasets. To satisfy this demand, we use an accelerated discovery of pneumonia, and following administration of the appropriate treatment may aid significantly in preventing patients’ conditions from deteriorating, which may eventually result in mortality ( Alharbi & Hosni Mahmoud, 2022 ). Several technologies, such as genetics and imaging, have emerged in recent decades to provide detailed healthcare information.…”