A Study of Machine Learning-Driven Diagnosis of Laryngeal Cancer from Narrow-Band Imaging and CT Scans

Abstract

Laryngeal Carcinoma (LC) represents the most widely malignancy in the neck and head region. It is associated with risk factors such as alcohol consumption, smoking, and exposure to environmentally harmful substances. In its early stages, this disease is difficult to diagnose, leading to a poor prognosis for patients. This research attempts to discover the potential for improving the diagnostic accuracy of laryngeal cancer using some techniques of Machine Learning (ML) and deep learning (DL). This work demonstrates the use of advanced models and mechanisms, including convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, to analyze laryngeal images taken from narrow-band imaging (NBI) computed tomography (CT). The study includes preprocessing methods such as normalization, denoising, segmentation, and data augmentation, which enhance the performance and effectiveness of the models. The project aims to evaluate the performance of models using metrics such as sensitivity, accuracy, specificity, and the F1 measure, so that it contributes to developing effective tools that help physicians make accurate data-driven diagnostic and treatment decisions. Ultimately, the research presents potential future applications of these technologies, based on integrated images and clinical data analysis, to predict disease progression and survival rates. These efforts will contribute to more effective tools for diagnosing and treating laryngeal cancer.