Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis  

作　　者：Walid El-Shafai Samy Abd El-Nabi El-Sayed MEl-Rabaie Anas M.Ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie 

机构地区：[1]Department of Electronics and Electrical Communications,Faculty of Electronic Engineering,Menoufia University,Menouf,32952,Egypt [2]Alexandria Higher Institute of Engineering&Technology(AIET),Alexandria,Egypt [3]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah Bint Abdulrahman University,Riyadh,84428,Saudi Arabia

出　　处：《Computers, Materials & Continua》2022年第3期6107-6125,共19页计算机、材料和连续体（英文）

基　　金：This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.

摘　　要：Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.

关 键 词：Medical images CADTra AD CT and X-ray images autoencoder 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]