DSpace Repository

Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach

Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach

Show full item record

Title: Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach
Author: Liang, Zhaohui
Abstract: Deep learning is the state-of-the-art artificial intelligence (AI) method for visual pattern detection and automated diagnosis. This paper describes the application of convolutional neural network (CNN), the deep learning model for visual recognition, to automatic detection of plasmodium parasitized red blood cells for malaria field screening and rapid diagnosis. The malaria thin blood smears are from Bangladesh and initially labeled by a specialist. 27,578 red blood cell images are segmented (raw set). The images are rotated clockwise three times to generate an augmented dataset with 110,312 red blood cell images. A 12-layer and an 18-layer CNN-based Malaria Net models are applied to classify both the raw data set and the augmented dataset. The performance is evaluated by ten-fold cross-validation and compared to a transfer learning model. In the ten-fold cross-validation test for Malaria Net, the average accuracy is 97.37% (18-layer) and 96.09% (12-layer) with the raw set, and is 97.93% and 96.75% with the augmented set, in comparison to 91.99% with the raw set and 94.26% with the augmented set in transfer learning. In addition, the two CNN models show superiority over transfer learning in all performance indicators such as sensitivity, specificity, precision, F1 score, and Matthews correlation coefficient. The Malaria Net can accurately detect malaria-infected red blood cells. A CNN model trained by domain-specific data shows superior performance over the transfer-learning method. Automatic image classification powered by deep learning offers not only an accurate method for the malaria field screening and rapid diagnosis but also a new solution for malaria control especially in resource-poor regions.
Subject: Medical imaging and radiology
Keywords: Convolutional neural networks
Deep learning
Medical image processing
Medical informatics
Information technology
Artificial intelligence
Machine learning
Type: Electronic Thesis or Dissertation
Rights: Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
URI: http://hdl.handle.net/10315/34319
Supervisor: Huang, Xiangji
Degree: MA - Master of Arts
Program: Information Systems and Technology
Exam date: 2017-06-16
Publish on: 2018-03-01

Files in this item



This item appears in the following Collection(s)