Hidden Markov Random Field Model Based VGG-16 for Segmentation and Classification of Head and Neck Cancer

Abstract

The head and neck squamous cell carcinoma (HNSCC) is a group of malignant tumors that typically originates in squamous cells lining mucous membranes of head and neck regions. This paper proposed a hidden markov random field model (HMRFM) and VGG-16 for effective segmentation and classification of head and neck cancer. The preprocessing is done by using the min-max normalization method which improves the model performance and feeds into the segmentation process. In segmentation, the HMRFM is utilized to effectively identify and isolate segments in sequential data which makes it easier to analyze the accurate data from input sequence. The VGG-16 model is fine-tuned by certain convolutional layers which is utilized due to less image dataset and overcome the overfitting issues. This method is evaluated on the HNSCC-3DCT-RT dataset and attains better results with regards to AUC, accuracy, sensitivity, specificity, and F1-score values about 95.2%, 98.7%, 91.4%, 97.5% and 91.5% correspondingly. The obtained result shows that the HMRFM with VGG-16 is better compared to other techniques like Deep CNN with data augmentation, 3D-unet convolutional neural network (CNN), and voted ensemble machine learning (VEML).