# An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue

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## Abstract

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## Simple Summary

## Abstract

## 1. Introduction

- The first stage of an AI-based system for multiclass grading of OSCC which can potentially improve objectivity and reproducibility of histopathological examination, as well as reduce the time necessary for pathological inspections.
- The second stage of an AI-based system for segmentation of tumor on epithelial and stromal regions which can assist the clinician in discovering new informative features. It has great potential in the quantification of qualitative clinic-pathological features in order to predict tumor invasion and metastasis.
- A new preprocessing methodology based on the stationary wavelet transform (SWT) is proposed to enhance high-frequency components in the case of multiclass classification and to extract low-level features in the case of semantic segmentation. This approach allows more effective predictions and improves the robustness of the entire AI-based system.

#### Related Work

## 2. Materials and Methods

#### 2.1. Dataset Description

#### 2.2. Preprocessing Method Based on Stationary Wavelet Transform and Mapping Function

- no decimation step—provides redundant information,
- better time-frequency localization, and
- translation-invariance.

#### 2.3. AI-Based Models

#### 2.3.1. Xception

#### 2.3.2. ResNet50 and −101

#### 2.3.3. MobileNetv2

#### 2.4. DeepLabv3+

#### 2.5. Evaluation Criteria

_{micro}can be calculated. TP represents true positives, i.e., cases where the predicted and actual values are positive. TN represents true negatives, cases where the actual and predicted values are negative. False negatives (FN) capture cases when the prediction is negative and the actual value is positive. Furthermore, FP represents false positives, where the prediction is positive, and the actual value is negative [52].

_{macro}is based on the calculation of TPR

_{macro}as well as FPR

_{macro}and can be calculated as follows

_{macro}and

_{-micro}measure will result in better classification performance of the model.

## 3. Results

_{macro}and

_{-micro}values in the case of ResNet50, ResNet101, and MobileNetv2 architectures. However, RMSprop optimizer in a combination with Xception architecture achieves the overall highest values of AUC

_{macro}, and

_{-micro}. Summarized mean values of performance measure along with corresponding standard deviation for each model architecture is shown in Table 5.

^{−6}. The training process in the second stage was performed with a learning rate of 0.0001 and the same learning rate decay of 1 × 10

^{−6}.

_{micro}performance measure was monitored during the process of optimizing. Each Bayesian iteration involved data preprocessing with a defined set of mapping function constants, model training process, and performance evaluation. After 25 steps of random exploration and 20 steps of Bayesian optimization, the best performing constant configuration was obtained as shown in Table 6.

## 4. Discussion

_{macro}and 0.942 AUC

_{micro}are achieved with a combination of Xception architecture and RMSprop optimizer. Furthermore, ResNet50 in a combination with the Adam optimizer showed AUC

_{macro}and

_{-micro}values of 0.871 and 0.864, respectively, which is slightly lower than ResNet101 performance (0.882 AUC

_{macro}and 0.890 AUC

_{micro}). However, ResNet101—Adam was worst-performing in terms of standard deviation with values of ±0.125, and ±0.112. Lowest values of standard deviation were obtained in the case of MobileNetv2 architecture in a combination with Adam optimizer.

_{macro}value of 0.947 and AUC

_{micro}value of 0.954 or higher. Moreover, when all results are summed up, it can be noticed that the highest values of performance measure are achieved using the proposed methodology with coefficient mapping function constants a, b, c, and d with values of 0.091, 0.0301, 0.0086, and 0.3444, respectively, and db2 as wavelet function. Performance of the proposed model in terms of AUC

_{macro}and AUC

_{micro}values is 0.963 ± 0.042 and 0.966 ± 0.027, respectively; therefore, it can be concluded that not only performance measure was increased, but also the values of standard deviation were decreased. A decrease in standard deviation value resulted in increased robustness of the model.

## 5. Conclusions

_{macro}and 0.966 AUC

_{micro}with the lowest standard deviation of ±0.042 and ± 0.027, respectively.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**OSCC group of well-differentiated OSCC (

**grade I**), moderately differentiated OSCC (

**grade II**) and poorly differentiated OSCC (

**grade III**) with magnification × 10.

**Figure 4.**Representation of SWT decomposition, wavelet coefficient mapping, and SWT reconstruction (L_D—low pass filter, H_D—high pass filter, LL—approximation coefficients, LH—horizontal coefficients, HL—vertical coefficients, HH—diagonal coefficients, and CM–coefficient mapping function).

**Figure 5.**The Xception architecture; first, the data propagate through entry flow (first box), then through middle flow (second box) and repeats eight times. In the end, data propagate through the third box which represents exit flow [44].

**Figure 7.**Comparison of mean AUC

_{macro}and

_{-micro}values of three different optimizers (SGD, ADAM, and RMSprop) on pre-trained models: (

**a**) ResNet50; (

**b**) ResNet101; (

**c**) Xception; and (

**d**) MobileNetv2.

**Figure 8.**SWT decomposition at level 1 using Haar wavelet along with coefficient mapping, and SWT reconstruction.

**Figure 9.**SWT decomposition at level 1 using Haar wavelet function. LL subband is used as an input image for semantic segmentation.

**Figure 10.**Visual representation of histopathology images, ground truth masks, preprocessed images, and semantic segmentation results. The first column represents samples of OSCC obtained by the clinician while the second column is corresponding ground truth mask. The third column represents samples after preprocessing which are afterwards used as input variables for semantic segmentation. Finally, the last column shows the prediction for three cases (Grade I, II, and III) where the black colour represents stromal tissue and the red colour represents epithelial tissue.

**Table 1.**Characteristic of the patients include sex, age, smoking habits, presence of metastases in the lymph nodes, and histological grade of carcinoma.

Characteristic of the Patients | % | |
---|---|---|

Sex | F | 35 |

M | 65 | |

Age | To 49 | 6 |

50–59 | 13 | |

60–69 | 58 | |

+70 | 23 | |

Smoking | Y | 69 |

N | 31 | |

Lymph Node Metastases | Y | 46 |

N | 54 | |

Histological Grade (G) | I | 50 |

II | 33 | |

III | 17 |

Hyperparameter | Possible Parameters |
---|---|

a | 0–0.1 |

b | 0–0.1 |

c | 0–0.1 |

d | 0.001–1 |

Wavelet function | Haar, sym2, db2, bior1.3 |

Layer | Output | Layers | ResNet50 | ResNet101 |
---|---|---|---|---|

Number of Repeating Layers | ||||

Conv1 | 112 × 112 | 7 × 7, 64, stride 2 | ×1 | ×1 |

3 × 3 max pool, stride 2 | ×1 | ×1 | ||

Conv2_x | 56 × 56 | 1 × 1, 64 | ×3 | ×3 |

3 × 3, 64 | ||||

1 × 1, 256 | ||||

Conv3_x | 28 × 28 | 1 × 1, 128 | ×4 | ×4 |

3 × 3, 128 | ||||

1 × 1, 512 | ||||

Conv4_x | 14 × 14 | 1 × 1, 256 | ×6 | ×23 |

3 × 3, 256 | ||||

1 × 1, 1024 | ||||

Conv5_x | 7 × 7 | 1 × 1, 512 | ×3 | ×3 |

3 × 3, 512 | ||||

1 × 1, 2048 | ||||

1 × 1 | Flatten | ×1 | ×1 | |

3-d Fully Connected | ||||

Softmax |

**Table 4.**MobileNetv2 architecture; each row represents a sequence of at least 1 identical layer, repeated n times. The number c of output channels is the same for each layer in the same sequence. The first layer of each sequence consists of a stride s while all the rest use stride 1. The expansion factor t is used for the input size.

Input | Operator | Expansion Factor (t) | Number of Output Channels (c) | Repeating Number (n) | Stride (s) |
---|---|---|---|---|---|

224 × 224 × 3 | conv2d | - | 32 | 1 | 2 |

112 × 112 × 32 | bottleneck | 1 | 16 | 1 | 1 |

112 × 112 × 16 | bottleneck | 6 | 24 | 2 | 2 |

56 × 56 × 24 | bottleneck | 6 | 32 | 3 | 2 |

28 × 28 × 32 | bottleneck | 6 | 64 | 4 | 2 |

14 × 14 × 64 | bottleneck | 6 | 96 | 3 | 1 |

14 × 14 × 96 | bottleneck | 6 | 160 | 3 | 2 |

7 × 7 × 160 | bottleneck | 6 | 320 | 1 | 1 |

7 × 7 × 320 | conv2d 1 × 1 | - | 1280 | 1 | 1 |

7 × 7 × 1280 | avgpool 7 × 7 | - | - | 1 | - |

1 × 1 × 1280 | fully connected (Softmax) | - | 3 | - |

**Table 5.**Performance of different algorithms using AUC

_{macro}and

_{-micro}as evaluation metrics along with standard deviation (σ).

Algorithm | AUC_{macro} ± σ | AUC_{micro} ± σ |
---|---|---|

ResNet50 | 0.871 ± 0.105 | 0.864 ± 0.090 |

ResNet101 | 0.882 ± 0.125 | 0.890 ± 0.112 |

Xception | 0.929 ± 0.087 | 0.942 ± 0.074 |

MobileNetv2 | 0.877 ± 0.062 | 0.900 ± 0.049 |

**Table 6.**Constants of coefficient mapping function obtained using Bayesian optimization along with corresponding 5-fold cross-validation performance.

Parameters | Xception + SWT | |||||
---|---|---|---|---|---|---|

a | b | c | d | Wavelet | AUC_{macro} ± σ | AUC_{micro} ± σ |

0.0084 | 0.0713 | 0.0599 | 0.0566 | sym2 | 0.956 ± 0.054 | 0.964 ± 0.040 |

0.0091 | 0.0301 | 0.0086 | 0.3444 | db2 | 0.963 ± 0.042 | 0.966 ± 0.027 |

0.0063 | 0.0021 | 0.0771 | 0.3007 | db2 | 0.947 ± 0.092 | 0.954 ± 0.069 |

0.0081 | 0.0933 | 0.0469 | 0.2520 | haar | 0.952 ± 0.056 | 0.958 ± 0.050 |

0.0053 | 0.0575 | 0.0649 | 0.1694 | bior1.3 | 0.962 ± 0.050 | 0.965 ± 0.046 |

**Table 7.**Performance of DeepLabv3+ with Xception_65 as backbone trained with data preprocessed with different wavelet functions.

mIOU ± σ | F1 ± σ | Accuracy ± σ | Precision ± σ | Sensitivity ± σ | Specificity ± σ | ||
---|---|---|---|---|---|---|---|

DeepLabv3+ & Xception_65 | Original | 0.864 ± 0.020 | 0.933 ± 0.058 | 0.934 ± 0.012 | 0.933 ± 0.019 | 0.967 ± 0.013 | 0.873 ± 0.017 |

sym2 | 0.874 ± 0.037 | 0.953 ± 0.016 | 0.939 ± 0.019 | 0.950 ± 0.025 | 0.956 ± 0.012 | 0.908 ± 0.040 | |

db2 | 0.876 ± 0.032 | 0.953 ± 0.016 | 0.940 ± 0.017 | 0.952 ± 0.019 | 0.955 ± 0.014 | 0.911 ± 0.031 | |

Haar | 0.879 ± 0.027 | 0.955 ± 0.014 | 0.941 ± 0.015 | 0.951 ± 0.018 | 0.958 ± 0.016 | 0.910 ± 0.026 | |

bior1.3 | 0.874 ± 0.030 | 0.953 ± 0.015 | 0.939 ± 0.016 | 0.948 ± 0.020 | 0.958 ± 0.021 | 0.904 ± 0.027 |

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**MDPI and ACS Style**

Musulin, J.; Štifanić, D.; Zulijani, A.; Ćabov, T.; Dekanić, A.; Car, Z.
An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. *Cancers* **2021**, *13*, 1784.
https://doi.org/10.3390/cancers13081784

**AMA Style**

Musulin J, Štifanić D, Zulijani A, Ćabov T, Dekanić A, Car Z.
An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. *Cancers*. 2021; 13(8):1784.
https://doi.org/10.3390/cancers13081784

**Chicago/Turabian Style**

Musulin, Jelena, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić, and Zlatan Car.
2021. "An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue" *Cancers* 13, no. 8: 1784.
https://doi.org/10.3390/cancers13081784