Detecting Laryngeal Cancer from CT SCAN images using Improvised Deep Learning based Mask R-CNN Model
Detecting Laryngeal Cancer from CT SCAN images
Laryngeal cancer effective diagnosis is a very critical task particularly in the initial phase of the development of the malignancies in the head as well as neck region of the pa-tients. The earlier diagnosis tools are becoming ineffective in the modern era due to many screening of people on daily basis and consuming a large amount of the time of clinicians. This Project is aims to resolving these aforementioned problems. In this work, an improvised approach has been used for laryngeal cancer and its related symptom identifications in real-time. This approach is based on the deep learning technique and consumes very minimal time in comparison to the previous models.
Dataset Used
Here it is used a new deep learn-ing-based Mask R-CNN model for the early detection of laryngeal cancer with a greater accuracy level. For the performance analysis of the proposed model, two diverse datasets namely the ImageNet and multiple CT scan imaging data in a real-time screening of the patients are useed for laryngeal cancer identification