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Recent advances show the wide-ranging applications of machine learning for solving multi-disciplinary problems in cancer cell growth detection, modeling cancer growths and treatments, etc. There is growing interests among the faculty and students at Clayton State University to study the applications of machine learning for medical imaging and propose new algorithms based on a recently funded NSF grant proposal in medical imaging, skin cancer detection, and associated smartphone apps and a web-based user-friendly diagnosis interface. We tested many available open-source ML algorithm-based software sets in Python as applied to medical image data processing, and modeling used to predict cancer growths and treatments. We study the use of ML concepts that promote efficient, accurate, secure computation over medical images, identifying and classifying cancer cells, and modeling the cancer cell growths. In this collaborative project with another university, we follow a holistic approach to data analysis leading to more efficient cancer detection based upon both cell analysis and image recognition. Here, we compare ML based software methods and analyze their detection accuracy. In addition, we acquire publicly available data of cancer cell image files and analyze using deep learning algorithms to detect benign and suspicious image samples. We apply the current pattern matching algorithms and study the available data with possible diagnosis of cancer types.
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Focal stack based image forgery localization
Image security is becoming an increasingly important issue due to advances in deep learning based image manipulations, such as deep image inpainting and deepfakes. There has been considerable work to date on detecting such image manipulations using improved algorithms, with little attention paid to the possible role that hardware advances may have for improving security. We propose to use a focal stack camera as a novel secure imaging device, to the best of our knowledge, that facilitates localizing modified regions in manipulated images. We show that applying convolutional neural network detection methods to focal stack images achieves significantly better detection accuracy compared to single image based forgery detection. This work demonstrates that focal stack images could be used as a novel secure image file format and opens up a new direction for secure imaging.
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- Award ID(s):
- 1838179
- PAR ID:
- 10397186
- Date Published:
- Journal Name:
- Applied Optics
- Volume:
- 61
- Issue:
- 14
- ISSN:
- 1559-128X
- Page Range / eLocation ID:
- 4030
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1364/AO.450654
