Renal Cancer Cell Nuclei Detection from Cytological Images Using Convolutional Neural Network for Estimating Proliferation Rate
Keywords:
Cell nucleus, Convolution neural network, Cytological images, Renal cancer,Abstract
The Cytological images play an essential role in monitoring the progress of cancer cell mutation. The proliferation rate of the cancer cell is the prerequisite for cancer treatment. It is hard to accurately identify the nucleus of the abnormal cell in a faster way as well as find the correct proliferation rate since it requires an in-depth manual examination, observation and cell counting, which are very tedious and time-consuming. The proposed method starts with segmentation to separate the background and object regions with K-means clustering. The small candidate regions, which contain cell region is detected based on the value of support vector machine automatically. The sets of cell regions are marked with selective search according to the local distance between the nucleus and cell boundary, whether they are overlapping or non-overlapping cell regions. After that, the selective segmented cell features are taken to learn the normal and abnormal cell nuclei separately from the regional convolutional neural network. Finally, the proliferation rate in the invasive cancer area is calculated based on the number of abnormal cells. A set of renal cancer cell cytological images is taken from the National Cancer Institute, USA and this data set is available for the research work. Quantitative evaluation of this method is performed by comparing its accuracy with the accuracy of the other state of the art cancer cell nuclei detection methods. Qualitative assessment is done based on human observation. The proposed method is able to detect renal cancer cell nuclei accurately and provide automatic proliferation rate.Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.