An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 22; p. 8834
• Main Authors: Sahoo, Pravat Kumar, Mishra, Sushruta, Panigrahi, Ranjit, Bhoi, Akash Kumar, Barsocchi, Paolo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.11.2022; MDPI
• Subjects: Accuracy; Cancer; Care and treatment; clinicians; CT imaging; Datasets; Deep Learning; Diagnosis; Endoscopy; Humans; Identification; ImageNet; Laryngeal cancer; Laryngeal Neoplasms - diagnostic imaging; Larynx; Medical prognosis; Neural Networks, Computer; Oncology, Experimental; Patients; Radiation therapy; Real time; Tomography; Tomography, X-Ray Computed - methods; deep learning; clinicians; laryngeal cancer; patients; ImageNet
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22228834

Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images.