Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection

Abstract

Millions of individuals are affected each year by lung cancer, a serious global health concern. It may also cause numerous potentially fatal pulmonary problems, including infections, hemorrhage, or collapse. Finding a consistent and an effective way to ascertain lung cancer using medical imaging techniques is one of the primary issues in medical image processing. The difficulty of this task stems from the fact that the regions of the lungs that are affected by cancer might differ greatly in expressions of their size, location, shape, and aesthetics. Identifying whether the identified area is benign (non-cancerous) or malignant (cancerous) is another difficult task. Finding the appropriate course of treatment for the patient will depend on this. A critical stage in the identification of lung malignancy is identifying the knobs that are expected to be malevolent. To solve these issues, in this study work we employ a deep learning methodology based on Mask region-based convolutional neural network (Mask-RCNN). For the purpose of identifying and locating infected lung regions on computed tomography (CT) scan images, model is built utilizing the customized Mask-RCNN. In accordance with the evaluation's findings, the model scored 99.32% for accuracy and 99.45% for mean DICE, respectively.