Hybrid Unified Deep Learning Network for Highly Precise Gleason Grading of Prostate Cancer.

Clicks: 347
ID: 82251
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #36 of 127 articles by views in conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 127 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Prostate cancer is one of the leading causes of death around the world. The manual Gleason grading of prostate cancer after histological analysis of stained tissue slides is rigorous, time-consuming and also suffers from subjectivity among experts. Image-based computer-assisted diagnosis can serve pathologists to efficiently diagnose cancer in early stages. We have proposed a Hybrid Unified Deep Learning Architecture to grade the prostate cancer accurately and quickly. For the feature analysis technique, we have implemented the shearlet transform in addition to original RGB images. We have introduced saliency maps of images using a Deep Convolutional Generative Adversarial Network (DCGAN) by applying semantic segmentation technique with the salient maps provided by pathology experts. Our proposed architecture is a combination of Convolutional Neural Netowork (CNN), Recurrent Neural Netowrk (RNN) and fine-tuned VGGnet. We have introduced a novel approach of utilizing LSTM-RNN for the sequential subband images of the shearlet coefficients. Our hybrid framework is a computationally high-cost architecture to train but proved to be highly accurate and faster in the testing phase. With our approach, we have achieved an accuracy of 0.98 ± 0.02 for Gleason grading of prostate cancer on the dataset provided by Jafari-Khouzani and Soltanian-Zadeh which is used in successive research work.
Reference Key
poojitha2019hybridconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Poojitha, Uthappa P;Lal Sharma, Shanker;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8856912
URL
Keywords
dissolution bioequivalence publication bias biowaiver biopharmaceutics classification system in silico pharmacokinetic simulations similarity factor

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.