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Rahman, S. Thesis approved__final format approved LW 8-1-2023.pdf (1.32 MB)

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Abstract Header

Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Classification and Segmentation

Rahman, M M Shaifur
http://orcid.org/0009-0007-9948-4074
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1691395550370526

Abstract Details

2023, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
Deep Convolutional Neural Networks demonstrate state-of-art performance in computer vision and medical image tasks. However, handling a large-scale image is still a challenging task that usually deals with resizing and patching methods to embed in the lower dimensional space. Recently, Learnable Resizer (LR) has been proposed to analyze large-scale images for computer vision tasks. This study proposes two DCNN models for classification and segmentation tasks constructed with LR in combination with successful classification and segmentation architectures. The performance of the proposed models is evaluated for the Diabetic Retinopathy (DR) analysis and skin cancer segmentation tasks. The proposed model demonstrated better performance than the existing methods for segmentation and classification tasks. For classification tasks, the proposed architectures achieved a 5.34% improvement in accuracy compared to ResNet50. Besides, around 0.62% accuracy over the base model and 0.28% in Intersection-over-Union (IoU) from state-of-the-art performance. The proposed model with the resizer network enhances the capability of the existing R2U-Net for medical image segmentation tasks. Moreover, the proposed methods enable a significant advantage in learning better with a few samples. The experimental results reveal that the proposed models are better than the current approaches.
Tarek M Taha (Committee Chair)
Eric Balster (Committee Member)
Chris Yakopcic (Committee Member)
59 p.
Artificial Intelligence; Biomedical Research; Computer Engineering; Computer Science; Engineering; Medical Imaging
Segmentation, Medical Image Segmentation, Medical Image Classification, Deep learning for large-scale images, large-scale image analysis, medical image with deep learning, artificial intelligence

Recommended Citations

Citations

  • Rahman, M. M. S. (2023). Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Classification and Segmentation [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1691395550370526

    APA Style (7th edition)

  • Rahman, M M Shaifur. Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Classification and Segmentation. 2023. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1691395550370526.

    MLA Style (8th edition)

  • Rahman, M M Shaifur. "Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Classification and Segmentation." Master's thesis, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1691395550370526

    Chicago Manual of Style (17th edition)

dayton1691395550370526
86
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.