Lung Nodule Classification Research Articles

Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions.

Accurate identification of malignant lung lesions is a prerequisite for rational clinical management to reduce morbidity and mortality of lung cancer. However, classification of lung nodules into malignant and benign cases is difficult as they show similar features in computer tomography and sometimes positron emission tomography imaging, making invasive tissue biopsies necessary. To address the challenges in evaluating indeterminate nodules, the authors investigate the molecular profiles of small extracellular vesicles (sEVs) in differentiating malignant and benign lung nodules via a liquid biopsy-based approach. Aiming to characterize phenotypes between malignant and benign groups, they develop a single-molecule-resolution-digital-sEV-counting-detection (DECODE) chip that interrogates three lung-cancer-associated sEV biomarkers and a generic sEV biomarker to create sEV molecular profiles. DECODE capturessEVs on a nanostructured pillar chip, confines individual sEVs, and profiles sEV biomarker expression through surface-enhanced Raman scattering barcodes. The author utilizeDECODE to generate a digitally acquiredsEV molecular profiles in a cohort of 33 people, including patients with malignant and benign lung nodules, and healthy individuals. Significantly, DECODE reveals sEV-specific molecular profiles that allow the separation of malignant from benign (area under the curve, AUC = 0.85), which is promising for non-invasive characterisation of lung nodules found in lung cancer screening and warrants further clinincal validaiton with larger cohorts.

Deep Learning Techniques to Diagnose Lung Cancer.

Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

Auto diagnostic system for detecting solitary and juxtapleural pulmonary nodules in computed tomography images using machine learning

Lung cancer is one of the most serious cancers in the world with the minimum survival rate after the diagnosis as it appears in Computed Tomography scans. Lung nodules may be isolated from (solitary) or attached to (juxtapleural) other structures such as blood vessels or the pleura. Diagnosis of lung nodules according to their location increases the survival rate as it achieves diagnostic and therapeutic quality assurance. In this paper, a Computer Aided Diagnosis (CADx) system is proposed to classify solitary nodules and juxtapleural nodules inside the lungs. Two main auto-diagnostic schemes of supervised learning for lung nodules classification are achieved. In the first scheme, (bounding box + Maximum intensity projection) and (Thresholding + K-means clustering) segmentation approaches are proposed then first- and second-order features are extracted. Fisher score ranking is also used in the first scheme as a feature selection method. The higher five, ten, and fifteen ranks of the feature set are selected. In the first scheme, Support Vector Machine (SVM) classifier is used. In the second scheme, the same segmentation approaches are used with Deep Convolutional neural networks (DCNN) which is a successful tool for deep learning classification. Because of the limited data sample and imbalanced data, tenfold cross-validation and random oversampling are used for the two schemes. For diagnosis of the solitary nodule, the first scheme with SVM achieved the highest accuracy and sensitivity 91.4% and 89.3%, respectively, with radial basis function and applying the (Thresholding + Kmeans clustering) segmentation approach and the higher 15 ranks of the feature set. In the second scheme, DCNN achieved the highest accuracy and sensitivity 96% and 95%, respectively, to detect the solitary nodule when applying the bounding box and maximum intensity projection segmentation approach. Receiver operating characteristic curve is used to evaluate the classifier’s performance. The max. AUC = 90.3% is achieved with DCNN classifier for detecting solitary nodules. This CAD system acts as a second opinion for the radiologist to help in the early diagnosis of lung cancer. The accuracy, sensitivity, and specificity of scheme I (SVM) and scheme II (DCNN) showed promising results in comparison to other published studies.

Classification of lung nodules based on the DCA-Xception network.

Developing deep learning networks to classify between benign and malignant lung nodules usually requires many samples. Due to the precious nature of medical samples, it is difficult to obtain many samples. To investigate and test a DCA-Xception network combined with a new data enhancement method to improve performance of lung nodule classification. First, the Wasserstein Generative Adversarial Network (WGAN) with conditions and five data enhancement methods such as flipping, rotating, and adding Gaussian noise are used to extend the samples to solve the problems of unbalanced sample classification and the insufficient samples. Then, a DCA-Xception network is designed to classify lung nodules. Using this network, information around the target is obtained by introducing an adaptive dual-channel feature extraction module, and the network learns features more accurately by introducing a convolutional attention module. The network is trained and validated using 274 lung nodules (154 benign and 120 malignant) and tested using 52 lung nodules (23 benign and 29 malignant). The experiments show that the network has an accuracy of 83.46% and an AUC of 0.929. The features extracted using this network achieve an accuracy of 85.24% on the K-nearest neighbor and random forest classifiers. This study demonstrates that the DCA-Xception network yields higher performance in classification of lung nodules than the performance using the classical classification networks as well as pre-trained networks.

Deep fusion of gray level co-occurrence matrices for lung nodule classification.

Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.

DeepJoint Segmentation-based Lung Segmentation and Hybrid Optimization-Enabled Deep Learning for Lung Nodule Classification

Lung cancer is an aggressive disease among all cancer-based diseases, because of causing huge mortality in humans. Thus, earlier discovery is a basic task for diagnosing lung cancer and it helps increase the survival rate. Computed tomography (CT) is a powerful imaging technique used to discover lung cancer. However, it is time-consuming for examining each CT image. This paper develops an optimized deep model for classifying the lung nodules. Here, the pre-processing is done using Region of Interest (ROI) extraction and adaptive Wiener filter. The segmentation is done using the DeepJoint model wherein distance is computed with a congruence coefficient for extracting the segments. The nodule identification is done by a grid-based scheme. The features such as Global Binary Pattern (GBP), Texton features, statistical features, perimeter and area, barycenter difference, number of slices, short axis and long axis and volume are considered. The lung nodule classification is done to classify part solid, solid nodules and ground-glass opacity (GGO) using Deep Residual Network (DRN), which is trained by the proposed Shuffled Shepard Sine–Cosine Algorithm (SSSCA). The developed SSSCA is generated by the integration of the Sine–Cosine Algorithm (SCA) and Shuffled Shepard Optimization Algorithm (SSOA). The proposed SSSCA-based DRN outperformed with the highest testing accuracy of 92.5%, sensitivity of 93.2%, specificity of 83.7% and [Formula: see text]-score of 81.5%.

Ensemble framework based on attributes and deep features for benign-malignant classification of lung nodule

Early detection and identification of malignant lung nodules improve the survival of lung cancer patients. The visual attributes such as subtlety, spiculation, and calcification of lung nodules play an important role in the diagnosis of malignancy. However, the gap between attributes and computation features is the main factor that restricts the performance of computer-aided diagnosis (CAD). Therefore, we propose a Fuse-Long Short-Term Memory-Convolutional Neural Network (F-LSTM-CNN) ensemble learning algorithm which incorporates visual attributes and deep features to classify benign and malignant nodules. First, the attribute features are obtained from clinical information while the deep features of nodules are extracted from the preprocessed computed tomography (CT) images. Second, the Fuse-Convolutional Neural Network (F-CNN) model is proposed for highlighting the essential role of attributes in the classification processing which integrates deep features and attribute features mapped through the transposed convolution. Meanwhile, the Fuse-Long Short-Term Memory (F-LSTM) model is proposed to focus on the specific deep features for classification via the affine transformation of attribute features. Finally, early identification of malignant lung nodules is conducted by fusing the prediction scores of the F-LSTM and F-CNN models. The experiments were conducted on the public lung nodule dataset (LIDC-IDRI) and achieved accuracy, sensitivity, and specificity of 0.955, 1, and 0.937 with an Area under the ROC Curve (AUC) of 0.995 for lung nodule classification. The experiment results show that the proposed F-LSTM-CNN ensemble learning model facilitates the interpretation of diagnostic data and helps radiologists to make decisions in clinical practice.

A new framework for multi-scale CNN-based malignancy classification of pulmonary lung nodules

A new framework for multi-scale CNN-based malignancy classification of pulmonary lung nodules

An efficient IISH-2D DCNN-based lung nodule classification using CT scan images

Lung cancer has been identified as the world’s leading cause of death. Precise detection and testing of lung nodules at an early stage are essential for the diagnosis of lung cancer, therefore, it is one of the best ways to prevent deaths of lung cancer. An automated nodule detection system provides a second opinion to radiologists during early diagnosis. Much existing research uses Deep Convolutional Networks (DCNNs) for lung nodules classification. However, DCNNs normally need careful tuning of hyperparameters to reveal their excellent performance. Although, with the increasing size of state-of-the-art convolutional neural networks, the evaluation cost of the traditional optimization algorithms has become deplorable in most cases. Also, Lung CT scans data have a data imbalance problem inherently. So, to solve those problems, this paper proposes IISH-2D DCNN for lung nodule classification. The proposed methodology consists of pre-processing and classification phases. In the pre-processing phase, the 3D-CT scan slice is converted into a 2D-slice, and then the nodule boundary is extracted by calculation of ROI. After that, the extracted boundary is given as input to the IISH-2D DCNN that classifies the lung nodules. The performance of the proposed methodology is compared to the existing works based on accuracy, sensitivity, and specificity metrics. Thus, the proposed model outperforms existing lung nodule classification methodologies with higher accuracy, sensitivity, and specificity that are 99.8%, 97%, and 99%, respectively. Also, the proposed methodology has fewer errors than the state-of-the-art methods. Hence, the suggested method attains better performance in lung nodule classification and proves to be more effective.

Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy.

Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included. A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn. There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22. We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.

Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.

Simple SummaryArtificial Intelligence (AI) algorithms can assist clinicians in their daily tasks by automatically detecting and/or classifying nodules in chest CT scans. Bias of such algorithms is one of the reasons why implementation of them in clinical practice is still not widely adopted. There is no published review on the bias that these algorithms may contain. This review aims to present different types of bias in such algorithms and present possible ways to mitigate them. Only then it would be possible to ensure that these algorithms work as intended under many different clinical settings.Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely.

Systematic review for lung cancer detection and lung nodule classification: Taxonomy, challenges, and recommendation future works

Abstract Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis. Artificial intelligence has played an essential role in the medical field in general and in analyzing medical images and diagnosing diseases in particular, as it can reduce human errors that can occur with the medical expert when analyzing medical image. In this research study, we have done a systematic survey of the research published during the last 5 years in the diagnosis of lung cancer classification of lung nodules in 4 reliable databases (Science Direct, Scopus, web of science, and IEEE), and we selected 50 research paper using systematic literature review. The goal of this review work is to provide a concise overview of recent advancements in lung cancer diagnosis issues by machine learning and deep learning algorithms. This article summarizes the present state of knowledge on the subject. Addressing the findings offered in recent research publications gives the researchers a better grasp of the topic. We checked all the characteristics, such as challenges, recommendations for future work were analyzed in detail, and the published datasets and their source were presented to facilitate the researchers’ access to them and use it to develop the results achieved previously.

Self-Supervised Transfer Learning Based on Domain Adaptation for Benign-Malignant Lung Nodule Classification on Thoracic CT.

The spatial heterogeneity is an important indicator of the malignancy of lung nodules in lung cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule objects hold richer discriminative information. However, for deep learning methods driven by massive data, effectively capturing the 3D discriminative features of nodules in limited labeled samples is a challenging task. Different from previous models that proposed transfer learning models in a 2D pattern or learning from scratch 3D models, we develop a self-supervised transfer learning based on domain adaptation (SSTL-DA) 3D CNN framework for benign-malignant lung nodule classification. At first, a data pre-processing strategy termed adaptive slice selection (ASS) is developed to eliminate the redundant noise of the input samples with lung nodules. Then, the self-supervised learning network is constructed to learn robust image representations from CT images. Finally, a transfer learning method based on domain adaptation is designed to obtain discriminant features for classification. The proposed SSTL-DA method has been assessed on the LIDC-IDRI benchmark dataset, and it obtains an accuracy of 91.07% and an AUC of 95.84%. These results demonstrate that the SSTL-DA model achieves quite a competitive classification performance compared with some state-of-the-art approaches.

Improving the performance of lung nodule classification by fusing structured and unstructured data

We investigated the issue of improving the classification performance for pulmonary nodules by learning the fusion features of structured and unstructured data. Current strategies for lung nodule classification, such as radiomics methods and deep learning approaches, all share the flaw of only using the unstructured data of patients, which is always a collection of medical images (e.g., computed tomography (CT) scans, X-rays, and pathological sections), while ignoring the structured data (e.g., baseline demographics, clinical characteristics, and laboratory examinations). However, from a clinical perspective, all of this information is required for accurate patient diagnosis. Therefore, to exploit all patient information, we addressed a more difficult problem: jointly modeling the multimodal patient data. Two models are proposed to combine structured and unstructured data. One employs deep learning with a softmax classifier (the structured and unstructured data fusion neural network (SUDFNN)), and the other implements an extreme gradient boosting (XGBoost) classifier (the structured and unstructured data fusion XGBoost (SUDFX)). The annotated structured data in the extensible markup language (XML) file from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database and the CT scans from the LUng Nodule Analysis 2016 (LUNA16) dataset were used to validate our model. The results show that the performance of the model is significantly improved when introducing the structured data, regardless of the nodule cube size and which classifier is used. The rationale for the improvement with the addition of structured features is provided. The optimal accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) values reached 0.936, 0.919, 0.956, and 0.971, respectively. Consequently, fusing structured and unstructured data can uncover more patient information and provide better decision support for the clinical diagnosis and treatment process, providing good application value and promotion prospects.

Detection and Classification of Lung Carcinoma using CT scans

Lung carcinoma, generally known as lung cancer, is the most common cause of cancer which is related to mortality worldwide. Lung carcinoma is an extremely complex problem to solve and Lung cancer patients appear to be the most vulnerable to SARS-CoOVID-19 infection early discovery, on the other hand, has a high rate of survivability. Lung carcinoma detection in computed tomography (CT) has emerged as an emerging research subject in the field of medical imaging systems in recent years. The ability to accurately detect the size and location of lung cancer plays a critical role in lung cancer diagnosis. As a result, there is a requirement to rapidly read, detect, classify and evaluate CT scans. In this paper, we suggest a method for detecting and classifying lung nodules (or lesions) using a multi-strategy system. It has two parts: nodule detection (finding nodules) and classification (classifying nodules into Benign / non-cancerous or Malignant / cancerous). Lung CT scan images are utilized to detect and classify lung nodules in this work. U-Net architecture is used to segment CT scans, while VGG Net is tested on 3D images derived from LUNA 16 and LIDC - IDRI. The U-Net and the VGG-Net results are combined in the final findings.

Research on classification of benign and malignant lung nodules based on three-dimensional multi-view squeeze-and-excitation convolutional neural network

Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.

Association between visual emphysema and lung nodules on low-dose CT scan in a Chinese Lung Cancer Screening Program (Nelcin-B3)

ObjectivesThis study aimed to evaluate the association between visual emphysema and the presence of lung nodules, and Lung-RADS category with low-dose CT (LDCT).MethodsBaseline LDCT scans of 1162 participants from a lung cancer screening study (Nelcin-B3) performed in a Chinese general population were included. The presence, subtypes, and severity of emphysema (at least trace) were visually assessed by one radiologist. The presence, size, and classification of non-calcified lung nodules (≥ 30 mm3) and Lung-RADS category were independently assessed by another two radiologists. Multivariable logistic regression and stratified analyses were performed to estimate the association between emphysema and lung nodules, Lung-RADS category, after adjusting for age, sex, BMI, smoking status, pack-years, and passive smoking.ResultsEmphysema and lung nodules were observed in 674 (58.0%) and 424 (36.5%) participants, respectively. Participants with emphysema had a 71% increased risk of having lung nodules (adjusted odds ratios, aOR: 1.71, 95% CI: 1.26–2.31) and 70% increased risk of positive Lung-RADS category (aOR: 1.70, 95% CI: 1.09–2.66) than those without emphysema. Participants with paraseptal emphysema (n = 47, 4.0%) were at a higher risk for lung nodules than those with centrilobular emphysema (CLE) (aOR: 2.43, 95% CI: 1.32–4.50 and aOR: 1.60, 95% CI: 1.23–2.09, respectively). Only CLE was associated with positive Lung-RADS category (p = 0.02). CLE severity was related to a higher risk of lung nodules (ranges aOR: 1.44–2.61, overall p < 0.01).ConclusionIn a Chinese general population, visual emphysema based on LDCT is independently related to the presence of lung nodules (≥ 30 mm3) and specifically CLE subtype is related to positive Lung-RADS category. The risk of lung nodules increases with CLE severity.Key Points• Participants with emphysema had an increased risk of having lung nodules, especially smokers.• Participants with PSE were at a higher risk for lung nodules than those with CLE, but nodules in participants with CLE had a higher risk of positive Lung-RADS category.• The risk of lung nodules increases with CLE severity.

The clinical impact of observer variability in lung nodule classification in children with Wilms tumour.

To investigate the extent to which observer variability of computed tomography (CT) lung nodule assessment may affect clinical treatment stratification in Wilms tumour (WT) patients, according to the recent Société Internationale d'Oncologie Pédiatrique Renal Tumour Study Group (SIOP-RTSG) UMBRELLA protocol. I: CT thoraces of children with WT submitted for central review were used to estimate size distribution of lung metastases. II: Scans were selected for blinded review by five radiologists to determine intra- and inter-observer variability. They assessed identical scans on two occasions 6months apart. III: Monte Carlo simulation (MCMC) was used to predict the clinical impact of observer variation when applying the UMBRELLA protocol size criteria. Lung nodules were found in 84 out of 360 (23%) children with WT. For 21 identified lung nodules, inter-observer limits of agreement (LOA) for the five readers were ±2.4 and ±1.4mm (AP diameter), ±1.9 and ±1.8mm (TS diameter) and ±2.0 and ±2.4mm (LS diameter) at assessments 1 and 2. Intra-observer LOA across the three dimensions were ±1.5, ±2.2, ±3.5, ±3.1 and ±2.6mm (readers 1-5). MCMC demonstrated that 17% of the patients with a 'true' nodule size of ≥3mm will be scored as <3mm, and 21% of the patients with a 'true' nodule size of <3mm will be scored as being ≥3mm. A significant intra-inter observer variation was found when measuring lung nodules on CT for patients with WT. This may have significant implications on treatment stratification, and thereby outcome, when applying a threshold of ≥3mm for a lung nodule to dictate metastatic status.

Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images

Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures.