Detecting Laryngeal Cancer Lesions From Endoscopy Images Using Deep Ensemble Model

To improve the chances of survival for a patient with laryngeal cancer, early detection is crucial. Currently, the standard diagnostic method involves an endoscopic examination of the larynx, followed by a biopsy and histological analysis by an oncologist, which can be subject to variability due to subjective evaluation. Therefore, there is a need for a faster and more accurate detection system that can replace the current manual examination. Recent research has shown that Deep Learning technology can assist in identifying laryngeal cancer, including precancerous and cancerous tumors, from endoscopic pictures. However, endoscopic image processing is a challenging task due to the highly dynamic nature of the endoscopic video, spectrum fluctuations, and numerous image interferences. To address this challenge, a Deep Ensemble Learning approach using convolutional neural networks (CNNs) and an effective image segmentation technique has been proposed. The suggested model has an overall accuracy of 98.12%.