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

Abstract

Lung 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).

RETRACTED ARTICLE: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

“…Liu et al [ 15 ] (2023) developed an accurate and efficient deep learning-based model for the detection of ground-glass opacity (GGO) on 3D lung CT images using a pyramid input augmented multi-scale Convolutional Neural Network (CNN). The developed pyramid input augmented multi-scale CNN model developed by augmenting the existing multi-scale CNN model with a pyramid input module.…”

IoT‐based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

“…The original data were classified using a variety of performance measures, including kappa, Matthews correlation coefficient (MCC), MSE, recall, specificity, score, precision, accuracy, and recall. The proposed technique was contrasted with different approaches to assess MPA [28], ASO [29], chaotic ASO (CASO) [27], and opposition‐based learning (OBL) performance [28].…”

“…The ASO [27] method is a heuristic algorithm that leverages the properties of molecular dynamics. It depicts the relationships between atomic restriction and interaction forces in various positions using atomic weights to calculate movements in the search space.…”

Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians

“…Lung cancer is a leading cause of death globally, highlighting the urgent need for the precise early detection of nodules in radiology. A study by Syed Musthafa, Sankar, Benil, and Rao addresses this need by proposing a novel hybrid ML approach for early lung nodule prognosis [61]. Leveraging advanced techniques such as snake swarm optimization in combination with a bat model (ISSO-B) and chaotic atom search optimization (CASO), along with a hybrid learning-based deep neural network classifier (L-DNN), the approach aims to improve detection accuracy [61].…”