Liver cancer ranks among the leading global causes of death. The process of detecting liver tumours from computerized tomography (CT) images is crucial for the detection and management of liver cancer. It takes a significant amount of time and effort to physically identify cancer tissue. So, in order to accurately identify the right therapy, a computer-aided diagnostic is utilised in the decision-making process. As a result, the primary goal of this work is to properly identify and categorise liver cancer using deep learning technique. The volumetric data of 1800 Computerized Tomography images were taken for processing. The proposed method involves pre-processing, segmentation, classification, and feature extraction. The segmentation is processed by the Spatial Fuzzy C-means clustering algorithm to detect the affected liver region. Then the feature extraction is carried out by the Bat Optimization (BO) algorithm. The classifiers of the CNN are employed to categorize the affected and unaffected regions in the liver CT images. The findings demonstrate that the Convolutional Neural Network method performs better than competing approaches and provides possibilities for liver tumour detection and classification. The proposed methods performance criteria, including accuracy, recall, precision, and F1-Score, are assessed, and then its superiority to various existing systems is clearly shown. According to simulation results, the overall rates for accuracy, recall, precision, and F1-Score were roughly 99.80%, 100%, 99.99%, and 99.10% compared to a number of previous studies.

Keywords: Liver Cancer; Bat Optimization; Spatial Fuzzy C-means Algorithm; CNN; CT Images

References

1. D Anwanwan., et al. “Challenges in liver cancer and possible treatment approaches”. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1 (2020): 188314.
2. J Calderaro., et al. “Molecular and histological correlations in liver cancer”. Journal of Hepatology3 (2019): 616-630.
3. YM Yang., et al. “Inflammation and liver cancer: molecular mechanisms and therapeutic targets”. in Seminars in liver disease, Thieme Medical Publishers (2019): 026-042.
4. D Guo., et al. “Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation”. European Journal of Radiology 117 (2019): 33-40.
5. A Kiani., et al. “Impact of a deep learning assistant on the histopathologic classification of liver cancer”. NPJ Digital Medicine1 (2020): 1-8.
6. L Ma., et al. “Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer”. Cancer Cell4 (2019): 418-430.
7. JN Kather and J Calderaro. “Development of AI-based pathology biomarkers in gastrointestinal and liver cancer”. Nature Reviews Gastroenterology and Hepatology 10 (2020): 591-592.
8. A Das., et al. “Detection of liver cancer using modified fuzzy clustering and decision tree classifier in CT images”. Pattern Recognition and Image Analysis 2 (2019): 201-211.
9. H Seo., et al. “Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images”. IEEE Transactions on Medical Imaging 5 (2019): 1316-1325.
10. X Dong., et al. “Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework”. IEEE Access 8 (2020): 129889-129898.
11. A Aghamohammadi., et al. “TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach”. Expert Systems with Applications 183 (2021): 115406.
12. SH Ahn., et al. “Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer”. Radiation Oncology1 (2019): 1-13.
13. V Hemalatha and C Sundar. “Automatic liver cancer detection in abdominal liver images using soft optimization techniques”. Journal of Ambient Intelligence and Humanized Computing5 (2021): 4765-4774.
14. F Özyurt., et al. “A novel liver image classification method using perceptual hash-based convolutional neural network”. Arabian Journal for Science and Engineering 4 (2021): 3173-3182.
15. M Furuzuki., et al. “A Detection Method for Liver Cancer Region Based on Faster R-CNN”. in 2019 19th International Conference on Control, Automation and Systems (ICCAS), IEEE (2021): 808-811.
16. ZM Zhang., et al. “Early diagnosis of hepatocellular carcinoma using machine learning method”. Frontiers in Bioengineering and Biotechnology 8 (2020): 254.
17. W Książek., et al. “A Novel Machine Learning Approach for Early Detection of Hepatocellular Carcinoma Patients”. Cognitive Systems Research 54 (2019): 116-127.
18. N Gruber., et al. “A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation”. arXiv (2020).
19. A Saito., et al. “Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning”. Modern Pathology2 (2021): 417-425.
20. X Wang., et al. “A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images”. Medical Image Analysis 17 (2021): 101914.
21. A Das., et al. “Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques”. Cognitive Systems Research 54 (2019): 165-175.
22. E Trivizakis., et al. “Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation”. IEEE J. Biomed. Health Inform.3 (2019): 923-930.
23. A Kaur., et al. “An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network”. Expert Systems With Applications 186 (2021): 115686.
24. W Wang., et al. “Deep Learning-Based Radiomics Models for Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images and Clinical Data”. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany: IEEE (2019): 4881-4884.
25. S Almotairi., et al. “Liver tumor segmentation in CT scans using modified SegNet”. Sensors5 (2020): 1516.
26. R Khdhir., et al. “Pancreatic Cancer Segmentation and Classification in CT Imaging using Antlion Optimization and Deep Learning Mechanism”. International Journal of Advanced Science and Computer Applications 3 (2023).

Citation

Citation: Mohammad Alamgeer. “Integration of Fuzzy Logic and Soft Computing Approaches for the Detection and Classification of Liver Cancer”.Acta Scientific Cancer Biology 8.6 (2024): 12-25.

Copyright

Copyright: © 2024 Mohammad Alamgeer. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.