International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Comparative analysis of CNN models for Liver Cancer: A Review |
|---|---|
| ABSTRACT | This project presents a deep learning based liver cancer detection system using a Convolutional Neural Network (CNN). CT scan images and MRI images are used as input to the system. Since CT images and MRI images contain noise and complex organ structures, a CNN model is trained to automatically extract important features such as edges, texture, and tumor patterns from the images. The CNN consists of convolution layers, activation functions, pooling layers, and fully connected layers that work together to classify the liver as cancerous or non-cancerous. The model learns both local and global features of liver tissues during training. This approach reduces human error and improves diagnostic accuracy. Experimental results show that CNN provides reliable and fast liver cancer detection, making it suitable for medical image analysis applications. |
| AUTHOR | MIRZA NOUSHABA MASRUR MIRZA SAIFULLAH, DR.S.N.KAKARWAL Department of CSE, ICEEM, Sambhajinagar, India Professor, Department of CSE, ICEEM, Sambhajinagar, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404015 |
| pdf/15_Comparative analysis of CNN models for Liver Cancer A Review.pdf | |
| KEYWORDS | |
| References | 1. I. M. Arias, H. J. Alter, J. L. Boyer, D. E. Cohen, D. A. Shafritz, S. S. Thorgeirsson, and A. W. Wolkoff, The Liver: Biology and Pathobiology. Hoboken, NJ, USA: Wiley, 2020. 2. R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, ‘‘Cancer statistics, 2023,’’ CA, A Cancer J. Clinicians, vol. 73, no. 1, pp. 17–48, Jan. 2023. 3. R. Ganesan, S. J. Yoon, and K. T. Suk, ‘‘Microbiome and metabolomics in liver cancer: Scientific technology,’’ Int. J. Mol. Sci., vol. 24, no. 1, p. 537, Dec. 2022. 4. D. Nam, J. Chapiro, V. Paradis, T. P. Seraphin, and J. N. Kather, ‘‘Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction,’’ JHEP Rep., vol. 4, no. 4, Apr. 2022, Art. no. 100443. 5. K. M. Napte and A. Mahajan, ‘‘Liver segmentation using marker controlled watershed transform,’’ Int. J. Electr. Comput. Eng. (IJECE), vol. 13, no. 2, p. 1541, Apr. 2023. 6. A. M. Anter and L. Abualigah, ‘‘Deep federated machine learning-based optimization methods for liver tumor diagnosis: A review,’’ Arch. Comput. Methods Eng., vol. 30, no. 5, pp. 3359–3378, Jun. 2023. 7. P.M. Paithane, Dr.S.N. Kakarwal, Published a chapter "Automatic Determination Number of Cluster for Multi Kernel NMKFCM Algorithm on Image Segmentation " in a book "Advances in Intelligent Systems and Computing", Springer, Volume, ISBN 978-3-030-16659-5, pp.870-879. 8. Rahul Mapari, Dr. S. Kakarwal, Dr. R.R. Deshmukh, Brain Tumor Classification and Segmentation using DTCW Transform, Back Propagation Neural Network and Spatial Fuzzy C-Means Clustering, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-9 Issue-5, Mar 2020, pp. 2073-2079 9. P.M. Paithane , Dr.S.N. Kakarwal, Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach, International Journal of Intelligent Systems and Applications in Engineering, Vol.10(1), 2022, ISSN: 2147-6799, pp.98-104 |
