**3. Performance analysis**

To select the best feature extractor from all the pretrained models, metrics such as F1- score and accuracy are considered. Higher accuracies may not be the most efficient and reliable metric always. Hence, F1-score is also considered as it shows individual class performance and is useful when the dataset is highly imbalanced. **Table 2** shows the overall accuracies obtained when all the pretrained models are used.

From **Table 2**, it is found that performance of DenseNet is better than the other deep learning architectures. The performance of the variants of DenseNet is given in **Table 3**. Here it is observed that with the increase in the number of layers of DenseNet from 121 to 201, there is a degradation in the accuracy. Hence, the F1 score is also affected.


#### **Table 2.**

*Performance of various pretrained models with SVM.*


#### **Table 3.**

*Performance of DenseNet variants.*


#### **Table 4.**

*Performance of DenseNet* −*121 with the classifiers.*

*Classification of Hepatocellular Carcinoma Using Machine Learning DOI: http://dx.doi.org/10.5772/intechopen.99841*


**Table 5.**

*Performance of DenseNet* −*121 with SVM.*

The final pretrained architecture selected for feature extraction is DenseNet −121 to be combined with the machine learning classifiers. Supervised algorithms such as decision tree, SVM, Naive bayes were taken into consideration to find the optimal classifier. The results of the feature extractor and classifier are given in **Table 4**. From **Table 4**, SVM is chosen to be the optimal classifier that works best with DenseNet −121 feature extractor.

DenseNet-121 is chosen due to high f1-score in spite of having less accuracy than DenseNet-169. Performance analysis of DenseNet-121 is given in **Table 5**.

#### **4. Conclusions and future work**

From the results obtained, it is observed that this method can provide better accuracy although the dataset is highly imbalanced and when there is a deficit in the dataset. Using convolution neural networks (CNN) can underperform when the dataset is imbalanced and it requires an extensive dataset to learn from. Improvements can be made by obtaining more data. Procuring more images from biopsies and medical data will help improve the system's efficiency and this can be extended as a separate component for the microscope.

### **Author details**

Lekshmi Kalinathan\*, Deepika Sivasankaran, Janet Reshma Jeyasingh, Amritha Sennappa Sudharsan and Hareni Marimuthu Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

\*Address all correspondence to: lekshmik@ssn.edu.in

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
