A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

Abstract

SummaryLung cancer is one of the major causes of death in the world, according to radiologists. However, a constant flow of medical images to hospitals is forcing radiologists to focus on accurate early prediction of nodules. Recently, several image‐processing techniques have cooperated for the early prediction of lung nodules. However, it's hard to detect strong nodes because of lung node diversity and environmental complexity. This study presents a hybrid machine learning technique for predicting an early prognosis of lung nodules from clinical images using a learning‐based neural network classifier. First, we introduce an improved snake swarm optimization with a bat model (ISSO‐B) for lung nodule segmentation using statistical information. Second, we demonstrate a chaotic atom search optimization (CASO) algorithm to select the optimal best features among multiple features, which minimize the dimensionality problem. Third, we develop a hybrid learning‐based deep neural network classifier (L‐DNN) for nodule prediction and classification. Finally, we evaluate our proposed technique with different public datasets LIDC‐IDRI and FAH‐GMU. Then, performance can be compared with the latest technology in terms of accuracy, sensitivity, specificity, and area under curve (AUC).