## 4 Conclusions  
Patients with liver cancer have a high mortality rate attributed to the late detection of the disease. Computer-aided diagnosis systems based on a variety of medical imaging techniques can help recognize liver cancer at an early stage. With the help of both conventional machine learning and deep learning classifiers, a variety of methods have been employed to identify liver cancer. The findings of this study suggest that using CNN to automatically extract features together with SVM classifier greatly improves classification performance. Furthermore, the findings suggest that employing our suggested hybrid model can greatly reduce the processing time, which is 22 s, when contrasted to ResNet50, which takes 32 s. All performance metrics accuracy, specificity, precision, and sensitivity reached 100%. Our approach can accurately and effectively recognize tumors, even in low-contrast CT images with respect to all quantitative assessments. Lastly, we can draw the following conclusions: (1) Deep learning model performance is extremely intriguing for use in medical equipment; the experimental result demonstrates significant improvement. Moreover, the suggested technique is unaffected by discrepancies in texture and intensity across demographics, imaging devices, patients, and settings; (2) the classifier distinguishes  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** & **AROC** \\ \hline [PERSON] et al. [15] & 98.11 & - & - & - \\ [PERSON] [16] & 98.5 & - & 98.2 & - \\ [PERSON] et al. [19] & 95.5 & 96.23 & 95.83 & 0.98 \\ The Proposed Model & **100** & **100** & **100** & **1** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the current study with the state of the art.  
Figure 14: Accuracy, Precision, Specificity, and Sensitivity of different approaches.  
the tumor with comparatively high precision; (3) segmentation of very small tumors is incredibly challenging, with the system being hyper-sensitive to contemplating local noise artifacts as potential tumors.  
The lack of large publicly available datasets forces CAD systems to use the available small private datasets generated from hospitals and scanning facilities. This implies that additional datasets should be made available for research and classification purposes. In the future, this work can be further extended using a large clinical dataset besides applying image processing techniques to enhance the visualization of images. Using a huge dataset, a reliable and trusted system can be built and employed in clinics.  
Conceptualization, K.A., H.A. and M.A.; methodology, A.A., H.A., Y.A.-I. and M.A.; software, H.A., M.A. and W.A.M.; validation, K.A., H.A., Y.A.-I., W.A.M. and M.A.; formal analysis, Y.A.-I., H.A., A.B. and M.A.; writing--original draft preparation, H.A., Y.A.-I., A.A., A.B. and W.A.M.; writing--review and editing, K.A., H.A., W.A.M., M.A. and Y.A.-I.; visualization, H.A.; supervision, H.A. and W.A.M.; project administration, H.A. and K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset analyzed during the current study was derived from the LiTS (Liver Tumor Segmentation Challenge (LiTS17)) organized in conjunction with ISBI 2017 and MICCAI 2017. Available online: [[https://www.kaggle.com/datasets/andrewmvd/liver-tumor-segmentation](https://www.kaggle.com/datasets/andrewmvd/liver-tumor-segmentation)]([https://www.kaggle.com/datasets/andrewmvd/liver-tumor-segmentation](https://www.kaggle.com/datasets/andrewmvd/liver-tumor-segmentation)) (accessed on 15 January 2022).  
The authors would thank the authors of the dataset for making it available online. Furthermore, they would like to thank the anonymous reviewers for their contribution towards enhancing this paper.  
The authors declare no conflict of interest.