Lung Tumour Segmentation Using Convolutional Neural Networks In Mri Images

Abstract

Lung Cancer Is A Serious Disease Occurring In Human Being. Medical Treatment Process Mainly Depends On Cancer Types And Its Location. It Is Possible To Save Many Precious Human Lives By Detecting Cancer Cells As Early As Possible. Developing An Automated Tool Is Essential To Detecting Malignant States At The Earliest Possible Stage Lung-Retina Net. The Accuracy Of Prediction Has Always Been A Challenge, Despite The Many Algorithms Proposed In The Past By Many Researchers. Using CNN Neural Networks, This Study Proposes A Methodology To Detect Abnormal Lung Tissue Growth A Multi-scale Feature Fusion-based Module. In Order To Achieve Great Accuracy, A Tool With A Higher Probability Of Detection Is Taken Into Account.. In Order To Overcome This Problem, CNN And RCNN Deep Learning Algorithms Have Been Proposed To Detect Classifications. Both The Region Proposal Network And The Classifier Network Use The Fused Lung-Retina Net’s Architecture As Their Base Layer. The Algorithm Achieves A Precision Of 98% In Detection And Classification. Based On Confusion Matrix Computation And Classification Accuracy Results, A Quantitative Analysis Of The Proposed Network Has Been Conducted.

Keywords