Classification and Detection of Ultrasound Liver Tumour Using VGG-ResNet

Registration ID: IJNRD_182251

Published ID: IJNRD2208030

DOI: Click Here to Get

Anu Susan Philip , Grace Mary Abraham , Gowri S Kumar , Rohith Balakrishnan

MATLAB, Convolutional Neural Network(CNN), Dataset, Learning Rate, Epoch Rate, and Minibatch Size

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.

Published Paper   E-Certificate

Anu Susan Philip, Grace Mary Abraham, Gowri S Kumar, & Rohith Balakrishnan (August-2022). Classification and Detection of Ultrasound Liver Tumour Using VGG-ResNet. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(8), 267-271. https://ijnrd.org/papers/IJNRD2208030.pdf

Volume 7 Issue 8, August-2022

Pages : 267-271

Paper Reg. ID: IJNRD_182251

Published Paper Id: IJNRD2208030

Downloads: 000121978

Research Area: Engineering

Country: Trivandrum, Kerala, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2208030.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2208030

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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