Dual Deep Learning and Feature-Based Models for Classification of Laryngeal Squamous Cell Carcinoma Using Narrow Band Imaging

Laryngeal Squamous Cell Carcinoma (LSCC) is a prevalent form of laryngeal cancer that originates from the mucosal surface of the larynx.The visual analysis of laryngeal tissue vascular patterns poses a significant challenge, as it heavily relies on the expertise and experience of medical practitioners.This paper proposes a dual approach for the early diagnosis of LSCC by employing a lightweight Deep Convolutional Neural Network (CNN) and statistical features.It further delves into feature visualization and interpretation of the proposed classification models.Methods: The initial step involves enhancing image quality through Contrast Limited Adaptive Histogram Equalization (CLAHE).In the first approach, we employ a modified SqueezeNet for classifying laryngeal tissues.In the second approach, we extract a combination of first-order statistical features -Percentile-25, Percentile-50, Percentile-75, Mean, and Standard Deviation of each RGB channeland second-order statistical features such as Contrast, Energy, Homogeneity, and Correlation from the Gray-Level Co-Occurrence Matrix (GLCM).These features are then classified using the Extreme Gradient Boosting (XGBoost) classification model.Results: The proposed models are trained and validated using an augmented publicly available dataset, prepared for both binary and multiclass classifications.The results indicate that the proposed models demonstrate exceptional accuracy and efficiency in classifying types of laryngeal cancer.