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Abnormally Detection in Medical Images Using Bag of Models

Wangad, Nileshkumar Sadanand
http://rave.ohiolink.edu/etdc/view?acc_num=dayton16396810455742

Abstract Details

2021, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Deep Learning Neural Networks provide smart alternatives and efficient algorithms on data-driven models for data processing. The approach of learning from image data can deliver reliable findings and analysis which in turn can give us more accurate results in less time with low utilization of resources. Through neural network implementation, we can get much better results in classification which other methods fail to replicate. Neural Network-based learning has shown to be beneficial in a variety of fields, and its application has aided us to consider the implementation of the COVID-19 epidemic. The most challenging task observed is the detection of COVID-19 symptoms. The most relevant type of detecting the symptoms is from human lung X-ray images without manual intervention. For this purpose, we implemented a novel algorithm to classify the positive epidemic cases from the dataset consisting of human X-rays labeled under covid and non-covid. The dataset is trained through a series of multilayer models such as VGG16, VGG19, Inception ResnetV2, Xception, and MobileNet to extract the feature and classify the images respectively, and then finally feed into a Support Vector Machine (SVM) classifier to get the desired output. The training yielded an accuracy of 95% from this approach and can be considered for further implementation on different research topics in the near future.
Tam Nguyen (Advisor)
Tom Ongwere (Committee Member)
James Buckley (Committee Member)
Artificial Intelligence; Health Care; Health Sciences; Information Technology; Medical Imaging

Recommended Citations

Citations

  • Wangad, N. S. (2021). Abnormally Detection in Medical Images Using Bag of Models [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton16396810455742

    APA Style (7th edition)

  • Wangad, Nileshkumar. Abnormally Detection in Medical Images Using Bag of Models. 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton16396810455742.

    MLA Style (8th edition)

  • Wangad, Nileshkumar. "Abnormally Detection in Medical Images Using Bag of Models." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton16396810455742

    Chicago Manual of Style (17th edition)

dayton16396810455742
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This open access ETD is published by University of Dayton and OhioLINK.