Detection of Tonsillopharyngitis with Grad-Cam and Optimization-Based Model Using Oropharyngeal Images

Year 2022, , 56 - 66, 27.12.2022

Muhammed Yıldırım , Orkun Eroğlu

https://doi.org/10.46572/naturengs.1205919

Abstract

Tonsillopharyngitis is a sudden onset contagious infection of the pharynx and tonsils. Patients experience a rapid general condition and loss of workforce. In addition to affecting patients, it spreads and affects other individuals. In addition, it causes severe complications and increases hospital costs. Therefore, early and accurate diagnosis is essential. In this study, a hybrid model is developed for the diagnosis of tonsillopharyngitis. First, the heat maps of the images in the original data set by applying the Gradient-weighted Class Activation Mapping (Grad-Cam) method. In the proposed model, feature maps are obtained from the original and heatmap datasets using the Darknet53 architecture as the base. It is aimed to increase the performance of the proposed model by bringing together different features of the same image. After the feature map obtained after the feature fusion step is optimized with the Relief method, classification is carried out using an SVM shallow classifier.

Keywords

Artificial Intelligence, CNN, Grad-Cam, Relief, Tonsillopharyngitis

References

• [1] Bochner, R.E., Gangar, M., Belamarich, P.F., A clinical approach to tonsillitis, tonsillar hypertrophy, and peritonsillar and retropharyngeal abscesses. Pediatrics in Review, 2017. 38(2): p. 81-92.
• [2] Vicedomini, D., et al., Diagnosis and management of acute pharyngotonsillitis in the primary care pediatrician's office. Minerva Pediatrica, 2014. 66(1): p. 69-76.
• [3] Demir, N., Bayar Muluk, N., Chua, D., Acute Tonsillopharyngitis in Children, in Pediatric ENT Infections. 2022, Springer. p. 515-523.
• [4] Osiejewska, A., et al., Acute tonsillopharyngitis-a review. Journal of Education, Health and Sport, 2022. 12(7): p. 873-882.
• [5] Amiraraghi, N., et al., Intramural oesophageal abscess: an unusual complication of tonsillitis. BMJ Case Reports CP, 2019. 12(2): p. bcr-2018-226010.
• [6] Bingol, H., NCA‐based hybrid convolutional neural network model for classification of cervical cancer on gauss‐enhanced pap‐smear images. International Journal of Imaging Systems and Technology, 2022.
• [7] Toğaçar, M., B. Ergen, Tümen, V., Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection. Biocybernetics and Biomedical Engineering, 2022.
• [8] Kiziloluk, S. and Sert, E., COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer. Medical and Biological Engineering & Computing, 2022. 60(6): p. 1595-1612.
• [9] Yoo, T.K., et al., Toward automated severe pharyngitis detection with smartphone camera using deep learning networks. Computers in biology and medicine, 2020. 125: p. 103980.
• [10] https://data.mendeley.com/datasets/8ynyhnj2kz/1.
• [11] Selvaraju, R.R., et al., Grad-cam: Visual explanations from deep networks via gradient-based localization. in Proceedings of the IEEE international conference on computer vision. 2017.
• [12] Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Communications of the ACM, 2017. 60(6): p. 84-90.
• [13] Redmon, J., Farhadi, A., Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
• [14] Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
• [15] Tan, M., Le, Q., Efficientnet: Rethinking model scaling for convolutional neural networks. in International conference on machine learning. 2019. PMLR.
• [16] Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
• [17] Sandler, M., et al. Mobilenetv2: Inverted residuals and linear bottlenecks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
• [18] He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
• [19] Zhang, X., et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
• [20] Yu, L., Liu, H., Feature selection for high-dimensional data: A fast correlation-based filter solution. in Proceedings of the 20th international conference on machine learning (ICML-03). 2003.
• [21] Joachims, T., 11 making large-scale support vector machine learning practical, in Advances in kernel methods: support vector learning. 1999, MIT press. p. 169.
• [22] Guo, G., et al. KNN model-based approach in classification. in OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". 2003. Springer.
• [23] Banfield, R.E., et al. A comparison of ensemble creation techniques. in International Workshop on Multiple Classifier Systems. 2004. Springer.
• [24] Cramer, G., Ford, R., Hall, R., Estimation of toxic hazard—a decision tree approach. Food and cosmetics toxicology, 1976. 16(3): p. 255-276.
• [25] Klecka, W.R., Iversen, G.R., Klecka, W.R., Discriminant analysis. Vol. 19. 1980: Sage.
• [26] Lewis, D.D. Naive (Bayes) at forty: The independence assumption in information retrieval. in European conference on machine learning. 1998. Springer.
• [27] Kleinbaum, D.G., et al., Logistic regression. 2002: Springer.
• [28] Yildirim, M., Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model. Concurrency and Computation: Practice and Experience, 2022: p. e7232.
• [29] Tümen, V., SpiCoNET: A Hybrid Deep Learning Model to Diagnose COVID-19 and Pneumonia Using Chest X-Ray Images. Traitement du Signal, 2022. 39(4).
• [30] Özbay, E., An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm. Artificial Intelligence Review, 2022. 1-28.
• [31] Eroglu O, Keles E., Karlidag T, Kaygusuz I, Turker C, Yalcin S., Review of Our Tonsillectomy Indications Firat Med J, 2018. 23 (4): 178-183.
• [32] Mazur, E., et al., Concurrent peritonsillar abscess and poststreptococcal reactive arthritis complicating acute streptococcal tonsillitis in a young healthy adult: a case report. BMC Infectious Diseases, 2015. 15(1): p. 1-5.