The exact lung cancer identification is a critical problem that has attracted the researchers’ attention. The practice of multiview single image and segmentation has been widely used for the last 2 years to improve the identification of lung cancer disease. The utilization of machine learning (ML) and deep learning (DL) techniques can significantly expedite the process of cancer detection and stage classification, enabling researchers to study a larger number of patients in a shorter time frame and at a reduced cost applying the image segmentation approach herein, the multiresolution rigid registration mechanism is applied to enhance the segmentation further. Techniques like principle component averaging and discrete wavelet transform are verified for the image fusion development. To review the performance of the suggested technique, the image database resource initiative‐based lungs image database consortium is tested in this paper which includes 4,682 computed tomography scan images of 61 patients with nodules sizes from 3 to 30 mm. According to the study finding, the outperformed results of our model are obtained in terms of feature mutual information, and peak signal‐to‐noise ratio, which were recorded at 0.80 and 19.25, respectively. Moreover, the detection and stages of cancer (STG‐1, STG‐2, STG‐3, and STG‐4) of lung nodules are also assessed by using the ResNet‐18 convolutional neural network classifier. With only 1.8 FP/scan, the achieved accuracy and sensitivity for detection are 98.2% and 96.4%, respectively. The study’s findings show that our proposed strategy outperforms existing models significantly. Therefore, the proposed models have the potential to be implemented in clinical settings to provide support to doctors in the early diagnosis of cancer, while minimizing the occurrence of false positives in scans.

Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.