# 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

- Torre, L.A.; Siegel, R.L.; Ward, E.M.; Jemal, A. Global Cancer Incidence and Mortality Rates and Trends—An Update. Cancer Epidemiol. Biomark. Prev.
**2015**, 25, 16–27. [Google Scholar] [CrossRef] [PubMed][Green Version] - Marur, S.; Forastiere, A.A. Head and Neck Cancer: Changing Epidemiology, Diagnosis, and Treatment. Mayo Clin. Proc.
**2008**, 83, 489–501. [Google Scholar] [CrossRef] [PubMed] - Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin.
**2021**. [Google Scholar] [CrossRef] [PubMed] - Bagan, J.; Sarrion, G.; Jimenez, Y. Oral cancer: Clinical features. Oral Oncol.
**2010**, 46, 414–417. [Google Scholar] [CrossRef] - Ganesh, D.; Sreenivasan, P.; Öhman, J.; Wallström, M.; Braz-Silva, P.H.; Giglio, D.; Kjeller, G.; Hasséus, B. Potentially Malignant Oral Disorders and Cancer Transformation. Anticancer Res.
**2018**, 38, 3223–3229. [Google Scholar] [CrossRef][Green Version] - Ettinger, K.S.; Ganry, L.; Fernandes, R.P. Oral Cavity Cancer. Oral Maxillofac. Surg. Clin. N. Am.
**2019**, 31, 13–29. [Google Scholar] [CrossRef] - Milas, Z.L.; Shellenberger, T.D. The Head and Neck Cancer Patient: Neoplasm Management. Oral Maxillofac. Surg. Clin. N. Am.
**2019**, 31. [Google Scholar] [CrossRef] - Warnakulasuriya, S.; Reibel, J.; Bouquot, J.; Dabelsteen, E. Oral epithelial dysplasia classification systems: Predictive value, utility, weaknesses and scope for improvement. J. Oral Pathol. Med.
**2008**, 37, 127–133. [Google Scholar] [CrossRef] - Mehlum, C.S.; Larsen, S.R.; Kiss, K.; Groentved, A.M.; Kjaergaard, T.; Möller, S.; Godballe, C. Laryngeal precursor lesions: Interrater and intrarater reliability of histopathological assessment. Laryngoscope
**2018**, 128, 2375–2379. [Google Scholar] [CrossRef] - Chen, H.; Sung, J.J.Y. Potentials of AI in medical image analysis in Gastroenterology and Hepatology. J. Gastroenterol. Hepatol.
**2021**, 36, 31–38. [Google Scholar] [CrossRef] - Stolte, S.; Fang, R. A survey on medical image analysis in diabetic retinopathy. Med Image Anal.
**2020**, 64, 101742. [Google Scholar] [CrossRef] - Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; van der Laak, J.A.; van Ginneken, B.; Sánchez, C.I. A survey on deep learning in medical image analysis. Med Image Anal.
**2017**, 42, 60–88. [Google Scholar] [CrossRef][Green Version] - Singh, A.; Sengupta, S.; Lakshminarayanan, V. Explainable Deep Learning Models in Medical Image Analysis. J. Imaging
**2020**, 6, 52. [Google Scholar] [CrossRef] - Haefner, N.; Wincent, J.; Parida, V.; Gassmann, O. Artificial intelligence and innovation management: A review, framework, and research agenda. Technol. Forecast. Soc. Chang.
**2021**, 162, 120392. [Google Scholar] [CrossRef] - Kaba, K.; Sarıgül, M.; Avcı, M.; Kandırmaz, H.M. Estimation of daily global solar radiation using deep learning model. Energy
**2018**, 162, 126–135. [Google Scholar] [CrossRef] - Lorencin, I.; Anđelić, N.; Mrzljak, V.; Car, Z. Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation. Energies
**2019**, 12, 4352. [Google Scholar] [CrossRef][Green Version] - Gurcan, M.N.; Boucheron, L.E.; Can, A.; Madabhushi, A.; Rajpoot, N.M.; Yener, B. Histopathological Image Analysis: A Review. IEEE Rev. Biomed. Eng.
**2009**, 2, 147–171. [Google Scholar] [CrossRef][Green Version] - Sharma, S.; Mehra, R. Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—A Comparative Insight. J. Digit. Imaging
**2020**, 33, 632–654. [Google Scholar] [CrossRef] - Wu, Z.; Wang, L.; Li, C.; Cai, Y.; Liang, Y.; Mo, X.; Lu, Q.; Dong, L.; Liu, Y. DeepLRHE: A Deep Convolutional Neural Network Framework to Evaluate the Risk of Lung Cancer Recurrence and Metastasis from Histopathology Images. Front. Genet.
**2020**, 11, 768. [Google Scholar] [CrossRef] - Tabibu, S.; Vinod, P.K.; Jawahar, C.V. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Sci. Rep.
**2019**, 9, 1–9. [Google Scholar] [CrossRef][Green Version] - Ariji, Y.; Fukuda, M.; Kise, Y.; Nozawa, M.; Yanashita, Y.; Fujita, H.; Katsumata, A.; Ariji, E. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg. Oral Med. Oral Pathol. Oral Radiol.
**2019**, 127, 458–463. [Google Scholar] [CrossRef] - Halicek, M.; Dormer, J.D.; Little, J.V.; Chen, A.Y.; Myers, L.; Sumer, B.D.; Fei, B. Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning. Cancers
**2019**, 11, 1367. [Google Scholar] [CrossRef][Green Version] - Horie, Y.; Yoshio, T.; Aoyama, K.; Yoshimizu, S.; Horiuchi, Y.; Ishiyama, A.; Hirasawa, T.; Tsuchida, T.; Ozawa, T.; Ishihara, S.; et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest. Endosc.
**2019**, 89, 25–32. [Google Scholar] [CrossRef] - Tamashiro, A.; Yoshio, T.; Ishiyama, A.; Tsuchida, T.; Hijikata, K.; Yoshimizu, S.; Horiuchi, Y.; Hirasawa, T.; Seto, A.; Sasaki, T.; et al. Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks. Dig. Endosc.
**2020**, 32, 1057–1065. [Google Scholar] [CrossRef] - Jeyaraj, P.R.; Nadar, E.R.S. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol.
**2019**, 145, 829–837. [Google Scholar] [CrossRef] - Bhandari, B.; Alsadoon, A.; Prasad, P.W.C.; Abdullah, S.; Haddad, S. Deep learning neural network for texture feature extraction in oral cancer: Enhanced loss function. Multimed. Tools Appl.
**2020**, 79, 1–24. [Google Scholar] [CrossRef] - Xu, S.; Liu, Y.; Hu, W.; Zhang, C.; Liu, C.; Zong, Y.; Chen, S.; Lu, Y.; Yang, L.; Ng, E.Y.K.; et al. An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks. IEEE Access
**2019**, 7, 158603–158611. [Google Scholar] [CrossRef] - Welikala, R.A.; Remagnino, P.; Lim, J.H.; Chan, C.S.; Rajendran, S.; Kallarakkal, T.G.; Zain, R.B.; Jayasinghe, R.D.; Rimal, J.; Kerr, A.R.; et al. Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer. IEEE Access
**2020**, 8, 132677–132693. [Google Scholar] [CrossRef] - Chan, C.-H.; Huang, T.-T.; Chen, C.-Y.; Lee, C.-C.; Chan, M.-Y.; Chung, P.-C. Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection. IEEE Trans. Biomed. Circuits Syst.
**2019**, 13, 766–780. [Google Scholar] [CrossRef] [PubMed] - Fraz, M.M.; Khurram, S.A.; Graham, S.; Shaban, M.; Hassan, M.; Loya, A.; Rajpoot, N.M. FABnet: Feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Comput. Appl.
**2020**, 32, 9915–9928. [Google Scholar] [CrossRef] - Das, N.; Hussain, E.; Mahanta, L.B. Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network. Neural Netw.
**2020**, 128, 47–60. [Google Scholar] [CrossRef] - El-Naggar, A.K.; Chan, J.K.; Takata, T.; Grandis, J.R.; Slootweg, P.J. WHO classification of head and neck tumours. Int. Agency Res. Cancer
**2017**. [Google Scholar] [CrossRef] - Amin, M.B.; Edge, S.B.; Greene, F.L.; Byrd, D.R.; Brookland, R.K.; Washington, M.K.; Gershenwald, J.E.; Compton, C.C.; Hess, K.R.; Sullivan, D.C.; et al. AJCC Cancer Staging Manual, 8th ed.; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
- Jakovac, H.; Stašić, N.; Krašević, M.; Jonjić, N.; Radošević-Stašić, B. Expression profiles of metallothionein-I/II and megalin/LRP-2 in uterine cervical squamous lesions. Virchows Archiv
**2021**, 478, 735–746. [Google Scholar] [CrossRef] - Shorten, C.; Khoshgoftaar, T.M. A survey on Image Data Augmentation for Deep Learning. J. Big Data
**2019**, 6, 60. [Google Scholar] [CrossRef] - Han, J.; Kamber, M.; Pei, J. Classification. In Data Mining; Elsevier: Amsterdam, The Netherlands, 2012; pp. 327–391. [Google Scholar]
- Addison, P.S. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance; CRC Press: Boca Raton, FL, USA, 2017. [Google Scholar] [CrossRef]
- Štifanić, D.; Musulin, J.; Miočević, A.; Šegota, S.B.; Šubić, R.; Car, Z. Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory. Complexity
**2020**, 2020, 1–12. [Google Scholar] [CrossRef] - Zhang, D. Wavelet transform. In Fundamentals of Image Data Mining; Springer: Cham, Switzerland, 2019; pp. 35–44. [Google Scholar] [CrossRef]
- Qayyum, H.; Majid, M.; Anwar, S.M.; Khan, B. Facial Expression Recognition Using Stationary Wavelet Transform Features. Math. Probl. Eng.
**2017**, 2017, 1–9. [Google Scholar] [CrossRef][Green Version] - Janani, S.; Marisuganya, R.; Nivedha, R. MRI image segmentation using Stationary Wavelet Transform and FCM algorithm. Int. J. Comput. Appl.
**2013**. [Google Scholar] [CrossRef][Green Version] - Feurer, M.; Hutter, F. Hyperparameter optimization. In Automated Machine Learning; Springer: Cham, Switzerland, 2019; pp. 3–33. [Google Scholar] [CrossRef][Green Version]
- Swersky, K.; Snoek, J.; Adams, R.P. Multi-task Bayesian optimization. In NIPS’13: Proceedings of the 26th International Conference on Neural Information Processing Systems 2004–2012; Curran Associates Inc.: Red Hook, NY, USA, 2013. [Google Scholar]
- Chollet, F. Xception: Deep Learning with Depthwise Separable Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 1251–1258. [Google Scholar]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar] [CrossRef][Green Version]
- Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; Chen, L.-C. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 4510–4520. [Google Scholar]
- Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell.
**2017**, 40, 834–848. [Google Scholar] [CrossRef] - Chen, L.C.; Papandreou, G.; Schroff, F.; Adam, H. Rethinking atrous convolution for semantic image segmentation. arXiv
**2017**, arXiv:1706.05587. [Google Scholar] - Chen, L.C.; Zhu, Y.; Papandreou, G.; Schroff, F.; Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 801–818. [Google Scholar] [CrossRef][Green Version]
- Choudhury, A.R.; Vanguri, R.; Jambawalikar, S.R.; Kumar, P. Segmentation of brain tumors using DeepLabv3+. In International MICCAI Brainlesion Workshop; Springer: Cham, Switzerland, 2018; pp. 154–167. [Google Scholar] [CrossRef]
- Tharwat, A. Classification assessment methods. Appl. Comput. Inform.
**2020**, 17, 168–192. [Google Scholar] [CrossRef] - Leonard, L. Web-Based Behavioral Modeling for Continuous User Authentication (CUA). In Advances in Computers; Elsevier: Amsterdam, The Netherlands, 2017; Volume 105, pp. 1–44. [Google Scholar]
- Rezatofighi, H.; Tsoi, N.; Gwak, J.; Sadeghian, A.; Reid, I.; Savarese, S. Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019; pp. 658–666. [Google Scholar]
- Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom.
**2020**, 21, 6. [Google Scholar] [CrossRef][Green Version] - Gunawardana, A.; Shani, G. A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res.
**2009**, 10. [Google Scholar] [CrossRef] - Mumtaz, W.; Ali, S.S.A.; Yasin, M.A.M.; Malik, A.S. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol. Eng. Comput.
**2018**, 56, 233–246. [Google Scholar] [CrossRef] - Speight, P.M.; Farthing, P.M. The pathology of oral cancer. Br. Dent. J.
**2018**, 225, 841–847. [Google Scholar] [CrossRef] - Li, L.; Pan, X.; Yang, H.; Liu, Z.; He, Y.; Li, Z.; Fan, Y.; Cao, Z.; Zhang, L. Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images. Multimed. Tools Appl.
**2018**, 79, 14509–14528. [Google Scholar] [CrossRef] - Al-Milaji, Z.; Ersoy, I.; Hafiane, A.; Palaniappan, K.; Bunyak, F. Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images. Pattern Recognit. Lett.
**2019**, 119, 214–221. [Google Scholar] [CrossRef] - Almangush, A.; Mäkitie, A.A.; Triantafyllou, A.; de Bree, R.; Strojan, P.; Rinaldo, A.; Hernandez-Prera, J.C.; Suárez, C.; Kowalski, L.P.; Ferlito, A.; et al. Staging and grading of oral squamous cell carcinoma: An update. Oral Oncol.
**2020**, 107, 104799. [Google Scholar] [CrossRef] - Mascitti, M.; Zhurakivska, K.; Togni, L.; Caponio, V.C.A.; Almangush, A.; Balercia, P.; Balercia, A.; Rubini, C.; Muzio, L.L.; Santarelli, A.; et al. Addition of the tumour–stroma ratio to the 8th edition American Joint Committee on Cancer staging system improves survival prediction for patients with oral tongue squamous cell carcinoma. Histopathology
**2020**, 77, 810–822. [Google Scholar] [CrossRef] - Heikkinen, I.; Bello, I.O.; Wahab, A.; Hagström, J.; Haglund, C.; Coletta, R.D.; Nieminen, P.; Mäkitie, A.A.; Salo, T.; Leivo, I.; et al. Assessment of Tumor-infiltrating Lymphocytes Predicts the Behavior of Early-stage Oral Tongue Cancer. Am. J. Surg. Pathol.
**2019**, 43, 1392–1396. [Google Scholar] [CrossRef] - Agarwal, R.; Chaudhary, M.; Bohra, S.; Bajaj, S. Evaluation of natural killer cell (CD57) as a prognostic marker in oral squamous cell carcinoma: An immunohistochemistry study. J. Oral Maxillofac. Pathol.
**2016**, 20, 173–177. [Google Scholar] [CrossRef][Green Version] - Fang, J.; Li, X.; Ma, D.; Liu, X.; Chen, Y.; Wang, Y.; Lui, V.W.Y.; Xia, J.; Cheng, B.; Wang, Z. Prognostic significance of tumor infiltrating immune cells in oral squamous cell carcinoma. BMC Cancer
**2017**, 17, 375. [Google Scholar] [CrossRef] [PubMed][Green Version] - Jonnalagedda, P.; Schmolze, D.; Bhanu, B. mvpnets: Multi-viewing path deep learning neural networks for magnification invariant diagnosis in breast cancer. In Proceedings of the 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan, 29–31 October 2018; pp. 189–194. [Google Scholar] [CrossRef]
- Silva, A.B.; Martins, A.S.; Neves, L.A.; Faria, P.R.; Tosta, T.A.; do Nascimento, M.Z. Automated nuclei segmentation in dysplastic histopathological oral tissues using deep neural networks. In Iberoamerican Congress on Pattern Recognition; Springer: Cham, Switzerland, 2019; pp. 365–374. [Google Scholar] [CrossRef]
- Fauzi, M.F.A.; Chen, W.; Knight, D.; Hampel, H.; Frankel, W.L.; Gurcan, M.N. Tumor Budding Detection System in Whole Slide Pathology Images. J. Med. Syst.
**2019**, 44, 38. [Google Scholar] [CrossRef] [PubMed] - Rashmi, R.; Prasad, K.; Udupa CB, K.; Shwetha, V. A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images. IEEE Access
**2020**, 8, 64331–64346. [Google Scholar] [CrossRef]

**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