An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission DOI Creative Commons
Shaik Karimullah, Mudassir Khan, Fahimuddin Shaik

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Oct. 7, 2024

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses pressing need for early detection through novel diagnostic approach that leverages innovative image processing techniques. The urgency is emphasized by alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, comprehensive diagnosis requires combination imaging research introduces an advanced tool implemented methodologies. methodology commences with histogram equalization, crucial step in artifact removal from CT images sourced medical database. Accurate segmentation, which vital diagnosis, follows. Otsu thresholding method optimization, employing Colliding Bodies Optimization (CBO), enhance precision segmentation process. A local binary pattern (LBP) deployed feature extraction, enabling identification nodule sizes precise locations. resulting underwent classification using densely connected CNN (DenseNet) deep learning algorithm, effectively distinguished between benign malignant tumors. proposed CBO+DenseNet exhibits remarkable performance improvements over methods. Notable enhancements accuracy (98.17%), specificity (97.32%), (97.46%), recall (97.89%) are observed, as evidenced results fractional randomized voting model (FRVM). These findings highlight potential tool. Its improved metrics promise heightened tumor localization. uniquely combines (CBO) DenseNet CNN, enhancing detection, setting it apart methods superior metrics.

Language: Английский

A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer DOI

Seyed Masoud HaghighiKian,

Ahmad Shirinzadeh-Dastgiri,

Mohammad Vakili-Ojarood

et al.

Indian Journal of Surgical Oncology, Journal Year: 2024, Volume and Issue: 16(1), P. 257 - 278

Published: Sept. 5, 2024

Language: Английский

Citations

7
Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models DOI

Umair Arif,

Chunxia Zhang,

Muhammad Waqas Chaudhary

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 14

Published: July 13, 2024

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There an urgent need develop effective lung prediction model that will help the early diagnosis and save patients from unnecessary treatments. The objective current paper meet extensiveness measure by using collaborative feature selection extraction methods enhance dendritic neural (DNM) comparison traditional machine learning (ML) models with minimum features boost accuracy, precision, sensitivity prediction. Comprehensive experiments on dataset comprising 1000 23 obtained Kaggle. Crucial are identified, proposed method's effectiveness evaluated metrics such as F1 score, sensitivity, specificity, confusion matrix against other ML models. Feature techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), Uniform Manifold Approximation Projection (UMAP) employed optimize performance. DNM accuracy at 96.50%, precision 96.64% 97.45% sensitivity. K-PCA explained 98.50%, 99.42%, 98.84% UMAP elaborated 98%, 98.82%, 98.82% approach showed outstanding performance enhancing model. Highlighting DNM's accurate cancer. These results emphasize potential contribute positively healthcare research providing better predictive outcomes.

Language: Английский

Citations

4
Lung cancer detection and classification using Optimized CNN features and Squeeze-Inception-ResNeXt model DOI

Geethu Lakshmi G,

P. Nagaraj

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 108437 - 108437

Published: March 1, 2025

Language: Английский

Citations

0
An Efficient Interpretable Stacking Ensemble Model for Lung Cancer Prognosis DOI

Umair Arif,

Chunxia Zhang,

Sajid Hussain

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108248 - 108248

Published: Oct. 16, 2024

Language: Английский

Citations

2
Traditional and advanced AI methods used in the area of neuro-oncology DOI
Soumyaranjan Panda,

Suman Sourav Biswal,

Sarit Samyak Rath

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 277 - 300

Published: Oct. 25, 2024

Language: Английский

Citations

1
An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission DOI Creative Commons
Shaik Karimullah, Mudassir Khan, Fahimuddin Shaik

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Oct. 7, 2024

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses pressing need for early detection through novel diagnostic approach that leverages innovative image processing techniques. The urgency is emphasized by alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, comprehensive diagnosis requires combination imaging research introduces an advanced tool implemented methodologies. methodology commences with histogram equalization, crucial step in artifact removal from CT images sourced medical database. Accurate segmentation, which vital diagnosis, follows. Otsu thresholding method optimization, employing Colliding Bodies Optimization (CBO), enhance precision segmentation process. A local binary pattern (LBP) deployed feature extraction, enabling identification nodule sizes precise locations. resulting underwent classification using densely connected CNN (DenseNet) deep learning algorithm, effectively distinguished between benign malignant tumors. proposed CBO+DenseNet exhibits remarkable performance improvements over methods. Notable enhancements accuracy (98.17%), specificity (97.32%), (97.46%), recall (97.89%) are observed, as evidenced results fractional randomized voting model (FRVM). These findings highlight potential tool. Its improved metrics promise heightened tumor localization. uniquely combines (CBO) DenseNet CNN, enhancing detection, setting it apart methods superior metrics.

Language: Английский

Citations

0