**Figure 4.**

*The training, loss and validation values over the training time.*

observe its ability to generalise and avoid overfitting. Throughout the training period, there were raised in the performance in the training set, which corresponded to a decrease in loss value. This behaviour is because the model is still learned to be able to generalise well; however, when the model is able to generalise, the validation loss fluctuates with close to the training loss. As shown in **Figure 4**, the proposed detector converges in the training process through the first 40 epochs, and there are wide fluctuations in performance in the training set, which correspond to fluctuations in loss value. This is expected behaviour during the first epochs of training since the model is still unstable; however, when the model stabilises, the training loss becomes steady with a slight variation close to the validation loss.

#### *4.2.2 Performance analysis*

A 2 2 confusion matrix was used to represent the prediction results of the cellular membranes. The matrix was built on two rows and two columns: membranes and non-membranes representing the classes. The 2 2 normalised confusion matrix is shown in **Figure 5**. To statistically analyse the behaviour of our detector at the pixel level, we calculated metrics from the test set using equations; 1, 2 and 3, as reported in **Table 2**. Whereas Dice-Coefficient was calculated at object level using eq. 4.

#### *4.2.3 Comparative analysis*

To our knowledge, there are few works that employed a deep learning models to segment cell membranes in IHC images. Hence, to validate our detector and to compare its performance with the state of the art segmentation methods, we first implemented some the public pre-trained models including FCN-8 [29], U-Net [30] and SegNet [31]. The comparative analysis of our detector against the other networks is reported in **Table 3**. When comparing the results produced by our detector with others, we derive that the proposed detector achieved good performance metrics. We get high performance than the popular networks; SegNet, U-Net and FCN-8. At pixellevel, it achieved an F-score value of 0.910. At the object level, it achieved a Dice score value of 0.829.

*Deep Learning-Based Segmentation of Cellular Membranes in Colorectal Immunohistochemical… DOI: http://dx.doi.org/10.5772/intechopen.108589*

#### **Figure 5.**

*Normalised confusion matrix for proposed detector.*


#### **Table 2.**

*The statistical metrics of our detector.*


#### **Table 3.**

*Comparative analysis of different models on the IHC dataset.*
