A deep learning approach for liver cancer detection in CT scans

Author:

Hameed Usman1,Ur Rehman Mujeeb1,Rehman Amjad2,Damaševičius Robertas3,Sattar Abdul1,Saba Tanzila2

Affiliation:

1. Institute of Computer Science, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, Pakistan

2. Artificial Intelligence & Data Analytics Lab (AIDA) CCIS, Prince Sultan University, Riyadh, Saudi Arabia

3. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland

Publisher

Informa UK Limited

Subject

Computer Science Applications,Radiology, Nuclear Medicine and imaging,Biomedical Engineering,Computational Mechanics

Reference54 articles.

1. TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach;Aghamohammadi A;Expert Syst Appl,2021

2. Diagnosis of liver tumor from CT images using digital image processing;Ali A;Int. J. Sci. Eng. Res,2015

3. Ali L, Hussain A, Li J, Shah A, Sudhakr U, Mahmud M, Zakir U, Yan X, Luo B, Rajak M (2014). Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver. In 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE). IEEE. doi:10.1109/cicare.2014.7007830

4. The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images

5. Amin J, Sharif M, Raza M, Saba T, & Rehman A (2019, April). Brain tumor classification: feature fusion. In 2019 international conference on computer and information sciences (ICCIS); Sakaka, Saudi Arabia. p. 1–6. IEEE.

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