|
|||||||||||||||
|
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
||||||||||||||
Issue: April 2024
Volume 9 | Issue 4
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 95
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Classification and Detection of Ultrasound Liver Tumour Using VGG-ResNet |
| Authors Name: | Anu Susan Philip , Grace Mary Abraham , Gowri S Kumar , Rohith Balakrishnan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_182251
|
| Published Paper Id: | IJNRD2208030 |
| Published In: | Volume 7 Issue 8, August-2022 |
| DOI: | |
| Abstract: | Since liver cancer is the most fatal kind of cancer, it's critical to catch it early. Due to the lack of symptoms, clinical procedures make early detection hard. Reading a large number of tumour images is a perilous work for radiologists. In contemporary processes, traditional methods are employed to determine if a tumour is malignant or benign. Certain malignancies are difficult to detect visually, which leads to a high percentage of false positives and negatives. Certain tumours have comparable traits, necessitating feature extraction-based classification and identification. Due to multiple challenges,such as low contrast between the liver and other organs and tumours,and sizes of tumours,and irregular tumour growth, the existing system has not been very good at segmenting the liver and lesions. As a result, a novel technique is required to solve these challenges. The existing challenges are addressed using a CNN-based multiclass detection approach. Several designs are compared, including GoogLeNet, Inception-v3, ResNet, and VGG-Net, with the VGG architecture being most accurate CNN-based multiclass identification. The RCNN principle is put into practise. The features were retrieved and fed into the RCNN. The CNN-based detection system has three stages: training, testing, and validation. Several factors such as kernel value, filter size, bias value, learning rate, and momentum can be changed to improve the accuracy of the recommended system. A novel architecture consisting of VGG-16 and ResNet-18 architecture was developed for the classification and detection for liver tumours. |
| Keywords: | MATLAB, Convolutional Neural Network(CNN), Dataset, Learning Rate, Epoch Rate, and Minibatch Size |
| Cite Article: | "Classification and Detection of Ultrasound Liver Tumour Using VGG-ResNet", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 8, page no.267-271, August-2022, Available :http://www.ijnrd.org/papers/IJNRD2208030.pdf |
| Downloads: | 000118753 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID:IJNRD2208030 Registration ID: 182251 Published In: Volume 7 Issue 8, August-2022 DOI (Digital Object Identifier): Page No: 267-271 Country: Trivandrum, Kerala, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2208030 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2208030 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn