Binary Classification of Laryngeal Images Utilising ResNet-50 CNN Architecture.

In India, laryngeal cancer is a significant health concern, underlining the critical need for early detection methods. This study introduces a novel approach to classify laryngeal lesions into nine morphological categories; due to data scarcity for all the nine classes, the data is divided into cancer and non-cancer classes, including both non-cancerous and Squamous Cell Carcinoma (SCC), by analysing endoscopy images with advanced convolutional neural networks, deep learning, and image processing techniques. A dataset of 1978 endoscopy images from 960 patients at a tertiary care center in Lucknow, between May 2015 and December 2023, was utilised for this purpose. These images, captured using an Olympus CV-170 processor and annotated via the CVAT tool, were processed to highlight Regions of Interest (ROI) for detailed examination. The dataset was split, with 90% for training/validation and 10% for testing. A total of 197 images out of 1978 were selected for testing, which included 43 cancerous and 154 non-cancerous images. For the feature extraction, ResNet50 was utilised. The model's evaluation through the Receiver Operating Characteristic (ROC) curve demonstrated high effectiveness, with areas of 0.95, 0.98, and 0.93 for combined, NBI-only, and WL-only datasets, respectively. The accuracy rates were notably high across all datasets, highlighting the potential of this model to significantly aid in the early detection and classification of laryngeal cancer. In India, where the incidence of head and neck cancer is high and there is a lack of both advanced instruments and expertise in Narrow Band Imaging (NBI), this model could be instrumental in the early detection of laryngopharyngeal cancer. Level of Evidence: 2 C.