ArticleSensors (Basel, Switzerland)2022
IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 63 citations in OpenAlex.
- Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data.Scientific reports · 2025Article
- Enriched lung cancer classification approach using an optimized hybrid deep learning approach.Scientific reports · 2025Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.Scientific reports · 2025Article
- An efficient smart phone application for wheat crop diseases detection using advanced machine learning.PloS one · 2025Article
- Strategic scheduling of the electric vehicle-based microgrids under the enhanced particle swarm optimization algorithm.Scientific reports · 2024Article
- Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing dysarthric speech recognition through SepFormer and hierarchical attention network models with multistage transfer learning.Scientific reports · 2024Article
- CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis.Scientific reports · 2024Article
- A Method for Real-Time Lung Nodule Instance Segmentation Using Deep Learning.Life (Basel, Switzerland) · 2024Article
- Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.Journal of imaging informatics in medicine · 2024Article
- Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics.Scientific reports · 2024Article
- Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.Scientific reports · 2024Article
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
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- Deep ocular tumor classification model using cuckoo search algorithm and Caputo fractional gradient descent.PeerJ. Computer science · 2024Article
- Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
Abstract
Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.