Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture

Author:

Soni Aradhana,Sethy Prabira Kumar,Dewangan Amit Kumar,Nanthaamornphong Aziz,Behera Santi Kumari,Devi Baishnu

Abstract

Abstract Problem Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment. Aim This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer. Methods A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture. Results The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field. Conclusion Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. WHO, Cancer Fact S. 2018. http://www.who.int/en/news-room/fact-sheets/detail/cancer (Access on 16th March 2023).

2. www.mouthcancerfoundation.org (Access on 15th. March 2023).

3. Iype EM, Pandey M, Mathew. A, Thomas. G, Sebastian P. Oral cancer among patients under the age of 35 years. J Postgrad Med. 2001;47:171.

4. Coletta RD, Yeudall WA, Salo T. Grand challenges in oral cancers. Front Oral Health. 2020;1:3. https://doi.org/10.3389/froh.2020.00003.

5. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

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