Feature Derivative‐Based Pixel Segmentation Method for Detecting Lung Tumors From CT Images

ABSTRACT Lung tumor segmentation using machine learning and artificial intelligence techniques leverages the diagnosis precision through accurate localization of the infections. The prominent factors are the features that reflect the infected region concealed through patterns, boundaries, and edges. In this article, a novel Feature‐Derivative Pixel Segmentation (FDPS) method is introduced to improve the tumor segmentation accuracy influenced by the disparity pixel distribution problem. This proposed method is assisted by tuneable recurrent learning (TRL) to vary the feature derivative count for varying segments. The learning inputs are modifiable using different extracted feature derivatives under parity and disparity pixel distributions. By identifying the maximum disparity pixels, the tuneable inputs for the recurrent learning are decided. The computation layer of the learning process identifies the maximum related regions identified under parity and disparity features. Such regions are segmented from multiple pixel distribution points until the image size. This process is therefore iterated to identify maximum conjoined features under different infected regions. The proposed method improves the accuracy by 9.63%, the true positive rate by 10.85%, and reduces the classification error by 10.06% for the maximum regions.