Application of InceptionV3, SqueezeNet, and VGG16 Convoluted Neural Networks in the Image Classification of Oral Squamous Cell Carcinoma: A Cross-Sectional Study

Abstract

BackgroundArtificial intelligence (AI) is a rapidly emerging field in medicine and has applications in diagnostics, therapeutics, and prognostication in various malignancies. The present study was conducted to analyze and compare the accuracy of three deep learning neural networks for oral squamous cell carcinoma (OSCC) images.Materials and methodsThree hundred and twenty-five cases of OSCC were included and graded histologically by two grading systems. The images were then analyzed using the Orange data mining tool. Three neural networks, viz., InceptionV3, SqueezeNet, and VGG16, were used for further analysis and classification. Positive predictive value, negative predictive value, specificity, sensitivity, area under curve (AUC), and accuracy were estimated for each neural network.ResultsHistological grading by Bryne's yielded significantly stronger inter-observer agreement. The highest accuracy was found for the classification of poorly differentiated squamous cell carcinoma images irrespective of the network used. Other values were variegated.ConclusionAI could serve as an adjunct for improvement in theragnostics. Further research is required to achieve the modification of mining tools for greater predictive values, sensitivity, specificity, AUC, accuracy, and security. Bryne's grading system is warranted for the better application of AI in OSCC image analytics.