21.7.3 Lungs, and Lung Cancer Image Analysis

Anantrasirichai, N., Hayes, W., Allinovi, M., Bull, D., Achim, A.,
Line Detection as an Inverse Problem: Application to Lung Ultrasound Imaging,
MedImg(36), No. 10, October 2017, pp. 2045-2056.
IEEE DOI 1710
Inverse problems, Lungs, Noise measurement, Radon, Speckle, Transforms, Ultrasonic imaging, ADMM, Line detection, deconvolution, image restoration, inverse problem, sparsity regularisation, ultrasound BibRef

Mittal, A.[Ajay], Hooda, R.[Rahul], Sofat, S.[Sanjeev],
Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning,
IET-IPR(11), No. 11, November 2017, pp. 937-952.
DOI Link 1711
BibRef

Zhang, Y.K.[Yuan-Ke], Rong, J.Y.[Jun-Yan], Lu, H.B.[Hong-Bing], Xing, Y.X.[Yu-Xiang], Meng, J.[Jing],
Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database,
MedImg(36), No. 12, December 2017, pp. 2510-2523.
IEEE DOI 1712
Computed tomography, Databases, Image restoration, Lungs, Principal component analysis, restoration BibRef

Gao, Y.F.[Yong-Feng], Liang, Z.R.[Zheng-Rong], Moore, W.[William], Zhang, H.[Hao], Pomeroy, M.J.[Marc J.], Ferretti, J.A.[John A.], Bilfinger, T.V.[Thomas V.], Ma, J.H.[Jian-Hua], Lu, H.B.[Hong-Bing],
A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images,
MedImg(38), No. 8, August 2019, pp. 1981-1992.
IEEE DOI 1908
Image reconstruction, Databases, Computed tomography, Radiology, Bayes methods, Image edge detection, Low dose CT, Bayesian image reconstruction BibRef

Yin, Y., Sedlaczek, O., Müller, B., Warth, A., González-Vallinas, M., Lahrmann, B., Grabe, N., Kauczor, H.U., Breuhahn, K., Vignon-Clementel, I.E., Drasdo, D.,
Tumor Cell Load and Heterogeneity Estimation From Diffusion-Weighted MRI Calibrated With Histological Data: an Example From Lung Cancer,
MedImg(37), No. 1, January 2018, pp. 35-46.
IEEE DOI 1801
biodiffusion, biomedical MRI, cancer, cellular biophysics, computerised tomography, deconvolution, image colour analysis, tumor cellularity BibRef

de Carvalho Filho, A.O.[Antonio Oseas], Corrêa Silva, A.[Aristofanes], Cardoso de Paiva, A.[Anselmo], Acatauassú Nunes, R.[Rodolfo], Gattass, M.[Marcelo],
Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network,
PR(81), 2018, pp. 200-212.
Elsevier DOI 1806
Lung cancer, Phylogenetic diversity index, Convolutional neural network BibRef

Oluyide, O.M.[Oluwakorede M.], Tapamo, J.R.[Jules-Raymond], Viriri, S.[Serestina],
Automatic lung segmentation based on Graph Cut using a distance-constrained energy,
IET-CV(12), No. 5, August 2018, pp. 609-615.
DOI Link 1807
BibRef

Novikov, A.A., Lenis, D., Major, D., Hladuvka, J., Wimmer, M., Bühler, K.,
Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs,
MedImg(37), No. 8, August 2018, pp. 1865-1876.
IEEE DOI 1808
Lung, Image segmentation, Heart, Task analysis, Biomedical imaging, Shape, Feature extraction, Lung segmentation, JSRT dataset BibRef

Netto, S.M.B., Bandeira Diniz, J.O., Silva, A.C., de Paiva, A.C., Nunes, R.A., Gattass, M.,
Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions,
IP(28), No. 4, April 2019, pp. 1813-1823.
IEEE DOI 1901
cancer, computerised tomography, feature extraction, image classification, lung, medical image processing, temporal analysis BibRef

Li, X.M.[Xiao-Mei], Dong, X.P.[Xiao-Peng], Lian, J.[Jian], Zhang, Y.[Yan], Yu, J.M.[Jin-Ming],
Knockoff filter-based feature selection for discrimination of non-small cell lung cancer in CT image,
IET-IPR(13), No. 3, February 2019, pp. 543-548.
DOI Link 1903
BibRef

Zhou, B., Chen, A., Crawford, R., Dogdas, B., Goldmarcher, G.,
A Progressively-Trained Scale-Invariant and Boundary-Aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions,
WACV19(1-10)
IEEE DOI 1904
computerised tomography, image segmentation, lung, medical image processing, neural nets, supervised learning, Training data BibRef

Eppenhof, K.A.J., Pluim, J.P.W.,
Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks,
MedImg(38), No. 5, May 2019, pp. 1097-1105.
IEEE DOI 1905
Training, Image registration, Strain, Lung, Biomedical imaging, Computed tomography, Convolutional neural networks, machine learning BibRef

Chen, G., Xiang, D., Zhang, B., Tian, H., Yang, X., Shi, F., Zhu, W., Tian, B., Chen, X.,
Automatic Pathological Lung Segmentation in Low-Dose CT Image Using Eigenspace Sparse Shape Composition,
MedImg(38), No. 7, July 2019, pp. 1736-1749.
IEEE DOI 1907
Shape, Lung, Image segmentation, Pathology, Computed tomography, Strain, discriminative appearance dictionary BibRef

Nezamabadi, K.[Kasra], Naseri, Z.[Zeinab], Abrishami Moghaddam, H.[Hamid], Modarresi, M.[Mohammadreza], Pak, N.[Neda], Mahdizade, M.[Mehrzad],
Lung HRCT pattern classification for cystic fibrosis using convolutional neural network,
SIViP(13), No. 6, September 2019, pp. 1225-1232.
Springer DOI 1908
BibRef

Liu, C.X.[Cai-Xia], Zhao, R.B.[Rui-Bin], Pang, M.Y.[Ming-Yong],
Lung segmentation based on random forest and multi-scale edge detection,
IET-IPR(13), No. 10, 22 August 2019, pp. 1745-1754.
DOI Link 1909
BibRef

Liu, Y.[Ying], Wang, H.D.[Hao-Dong], Gu, Y.[Yue], Lv, X.H.[Xiao-Hong],
Image classification toward lung cancer recognition by learning deep quality model,
JVCIR(63), 2019, pp. 102570.
Elsevier DOI 1909
Image classification, Cancer recognition, Deep feature, CNN BibRef

Saad, M.[Maliazurina], Lee, I.H.[Ik Hyun], Choi, T.S.[Tae-Sun],
Automated delineation of non-small cell lung cancer: A step toward quantitative reasoning in medical decision science,
IJIST(29), No. 4, 2019, pp. 561-576.
DOI Link 1911
collinearity, computer-aided delineation, convexity, non-small cell lung cancer, spatial analysis, topological processing BibRef

Seo, J.K.[Jin Keun], Kim, K.C.[Kang Cheol], Jargal, A.[Ariungerel.], Lee, K.[Kyounghun.], Harrach, B.[Bastian],
A Learning-Based Method for Solving Ill-Posed Nonlinear Inverse Problems: A Simulation Study of Lung EIT,
SIIMS(12), No. 3, 2019, pp. 1275-1295.
DOI Link 1911
BibRef

Khan, M.A.[M. Attique], Rubab, S., Kashif, A.[Asifa], Sharif, M.I.[Muhammad Imran], Muhammad, N.[Nazeer], Shah, J.H.[Jamal Hussain], Zhang, Y.D.[Yu-Dong], Satapathy, S.C.[Suresh Chandra],
Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection,
PRL(129), 2020, pp. 77-85.
Elsevier DOI 2001
Lungs cancer, Contrast normalization, Multiple features, Fusion, Selection BibRef

Kumar, A., Fulham, M., Feng, D., Kim, J.,
Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer,
MedImg(39), No. 1, January 2020, pp. 204-217.
IEEE DOI 2001
Computed tomography, Tumors, Lung, Image segmentation, Biomedical imaging, Cancer, Multi-modality imaging, deep learning, PET-CT BibRef

Bhandary, A.[Abhir], Prabhu, G.A.[G. Ananth], Rajinikanth, V., Thanaraj, K.P.[K. Palani], Satapathy, S.C.[Suresh Chandra], Robbins, D.E.[David E.], Shasky, C.[Charles], Zhang, Y.D.[Yu-Dong], Tavares, J.M.R.S.[João Manuel R.S.], Raja, N.S.M.[N. Sri Madhava],
Deep-learning framework to detect lung abnormality: A study with chest X-Ray and lung CT scan images,
PRL(129), 2020, pp. 271-278.
Elsevier DOI 2001
BibRef

Xue, P., Dong, E., Ji, H.,
Lung 4D CT Image Registration Based on High-Order Markov Random Field,
MedImg(39), No. 4, April 2020, pp. 910-921.
IEEE DOI 2004
Lung, Image registration, Computed tomography, Optimization, Strain, Topology, Image registration, 4D CT, multi-level processing strategy BibRef

Ozdemir, O., Russell, R.L., Berlin, A.A.,
A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans,
MedImg(39), No. 5, May 2020, pp. 1419-1429.
IEEE DOI 2005
Solid modeling, Lung, Cancer, Computed tomography, Deep learning, Uncertainty, Machine learning, image classification BibRef

Lakshmi, D., Thanaraj, K.P.[K. Palani], Arunmozhi, M.,
Convolutional neural network in the detection of lung carcinoma using transfer learning approach,
IJIST(30), No. 2, 2020, pp. 445-454.
DOI Link 2005
convolution neural network, transfer learning approach BibRef

Bansal, G.[Gaurang], Chamola, V.[Vinay], Narang, P.[Pratik], Kumar, S.[Subham], Raman, S.[Sundaresan],
Deep3DSCan: Deep residual network and morphological descriptor based framework for lung cancer classification and 3D segmentation,
IET-IPR(14), No. 7, 29 May 2020, pp. 1240-1247.
DOI Link 2005
BibRef

Shao, W., Patton, T.J., Gerard, S.E., Pan, Y., Reinhardt, J.M., Durumeric, O.C., Bayouth, J.E., Christensen, G.E.,
N-Phase Local Expansion Ratio for Characterizing Out-of-Phase Lung Ventilation,
MedImg(39), No. 6, June 2020, pp. 2025-2034.
IEEE DOI 2006
CT, functional avoidance, image registration, lung, out-of-phase ventilation, radiation therapy BibRef

Chen, B.Z.[Bing-Zhi], Zhang, Z.[Zheng], Lin, J.Y.[Jian-Yong], Chen, Y.[Yi], Lu, G.M.[Guang-Ming],
Two-stream collaborative network for multi-label chest X-ray Image classification with lung segmentation,
PRL(135), 2020, pp. 221-227.
Elsevier DOI 2006
Two-stream collaborative network, Lung segmentation, Self-adaptive weighted fusion, Multi-label CXR image classification BibRef

Priya, M.M.A.[Michael Mary Adline], Jawhar, S.J.[S. Joseph],
Advanced lung cancer classification approach adopting modified graph clustering and whale optimisation-based feature selection technique accompanied by a hybrid ensemble classifier,
IET-IPR(14), No. 10, August 2020, pp. 2204-2215.
DOI Link 2008
BibRef

Wang, X., Chen, H., Gan, C., Lin, H., Dou, Q., Tsougenis, E., Huang, Q., Cai, M., Heng, P.,
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis,
Cyber(50), No. 9, September 2020, pp. 3950-3962.
IEEE DOI 2008
Cancer, Lung, Feature extraction, Tumors, Task analysis, Supervised learning, Image analysis, Deep learning, whole slide images (WSIs) BibRef

Jena, S.R.[Sanjukta Rani], George, S.T.[Selvaraj Thomas],
Morphological feature extraction and KNG-CNN classification of CT images for early lung cancer detection,
IJIST(30), No. 4, 2020, pp. 1324-1336.
DOI Link 2011
automatic detection, CLAHE, CT, kernel based non-Gaussian convolutional neural networks, ROI BibRef

Schaff, F., Morgan, K.S., Pollock, J.A., Croton, L.C.P., Hooper, S.B., Kitchen, M.J.,
Material Decomposition Using Spectral Propagation-Based Phase-Contrast X-Ray Imaging,
MedImg(39), No. 12, December 2020, pp. 3891-3899.
IEEE DOI 2012
X-ray imaging, Attenuation, Lung, Approximation algorithms, Mathematical model, Radiography, Inverse methods, lung imaging, dual-energy BibRef

Kircher, M., Elke, G., Stender, B., Hernández Mesa, M., Schuderer, F., Dössel, O., Fuld, M.K., Halaweish, A.F., Hoffman, E.A., Weiler, N., Frerichs, I.,
Regional Lung Perfusion Analysis in Experimental ARDS by Electrical Impedance and Computed Tomography,
MedImg(40), No. 1, January 2021, pp. 251-261.
IEEE DOI 2012
Lung, Impedance, Ventilation, Computed tomography, Animals, Pulmonary perfusion imaging, EIT, MDCT, first pass kinetics BibRef

Cai, N., Chen, H., Li, Y., Peng, Y., Li, J.,
Adaptive Weighting Landmark-Based Group-Wise Registration on Lung DCE-MRI Images,
MedImg(40), No. 2, February 2021, pp. 673-687.
IEEE DOI 2102
Strain, Principal component analysis, Lung, Magnetic resonance imaging, Time series analysis, robust principal component analysis BibRef

Xiao, N.[Ning], Qiang, Y.[Yan], Zhao, Z.J.[Zi-Juan], Zhao, J.J.[Juan-Juan], Lian, J.H.[Jian-Hong],
Tumour growth prediction of follow-up lung cancer via conditional recurrent variational autoencoder,
IET-IPR(14), No. 15, 15 December 2020, pp. 3975-3981.
DOI Link 2103
BibRef

Rani, K.V.[K. Vijila], Dayana, C.T.[C. Thinkal], Therese, P.S.[P. Sujatha], Prince, M.E.[M. Eugine],
Triple novelty block detection and classification approach for lung tumor analysis,
IJIST(31), No. 2, 2021, pp. 1034-1049.
DOI Link 2105
bilateral filter, CAD, firefly search-based Macqueen's K-means clustering, texture feature BibRef

Taphorn, K.[Kirsten], Mechlem, K.[Korbinian], Sellerer, T.[Thorsten], de Marco, F.[Fabio], Viermetz, M.[Manuel], Pfeiffer, F.[Franz], Pfeiffer, D.[Daniela], Herzen, J.[Julia],
Direct Differentiation of Pathological Changes in the Human Lung Parenchyma With Grating-Based Spectral X-ray Dark-Field Radiography,
MedImg(40), No. 6, June 2021, pp. 1568-1578.
IEEE DOI 2106
Lung, Imaging, Gratings, X-ray imaging, Pulmonary diseases, Sensitivity, Correlation, Imaging modalities, lung, x-ray imaging and computed tomography BibRef

Viermetz, M.[Manuel], Gustschin, N.[Nikolai], Schmid, C.[Clemens], Haeusele, J.[Jakob], Noël, P.B.[Peter B.], Proksa, R.[Roland], Löscher, S.[Stefan], Koehler, T.[Thomas], Pfeiffer, F.[Franz],
Technical Design Considerations of a Human-Scale Talbot-Lau Interferometer for Dark-Field CT,
MedImg(42), No. 1, January 2023, pp. 220-232.
IEEE DOI 2301
Gratings, Computed tomography, Geometry, Imaging, Detectors, X-ray imaging, Lung, Computed tomography, dark-field contrast BibRef

Zheng, S.H.[Shao-Hua], Nie, W.Y.[Wei-Yu], Pan, L.[Lin], Zheng, B.[Bin], Shen, Z.Q.[Zhi-Qiang], Huang, L.Q.[Li-Qin], Pei, C.H.[Chen-Hao], She, Y.H.[Yu-Hang], Chen, L.Q.[Liu-Qing],
A dual-attention V-network for pulmonary lobe segmentation in CT scans,
IET-IPR(15), No. 8, 2021, pp. 1644-1654.
DOI Link 2106
BibRef

Doddavarapu, V.N.S.[V. N. Sukanya], Kande, G.B.[Giri Babu], Rao, B.P.[B. Prabhakar],
Rotational invariant fractional derivative filters for lung tissue classification,
IET-IPR(15), No. 10, 2021, pp. 2202-2212.
DOI Link 2108
BibRef

Chilakala, L.R.[Lokanath Reddy], Kishore, G.N.[Gattim Naveen],
Optimal deep belief network with opposition-based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: A DBNGHHB approach,
IJIST(31), No. 3, 2021, pp. 1404-1423.
DOI Link 2108
computed tomography (CT), deep belief networks (DBN), grasshopper and honey bee (GHHB) optimization algorithm, local tangent space alignment (LTSA) BibRef

Yin, L.[Li], Liu, Y.[Yang], Pei, M.T.[Ming-Tao], Li, J.R.[Jin-Rang], Wu, M.[Mukun], Jia, Y.Y.[Yuan-Yuan],
Laryngoscope8: Laryngeal image dataset and classification of laryngeal disease based on attention mechanism,
PRL(150), 2021, pp. 207-213.
Elsevier DOI 2109
Laryngeal image dataset, Laryngeal disease classification, Attention mechanism BibRef

Yang, J.[Jie], Angelini, E.D.[Elsa D.], Balte, P.P.[Pallavi P.], Hoffman, E.A.[Eric A.], Austin, J.H.M.[John H. M.], Smith, B.M.[Benjamin M.], Barr, R.G.[R. Graham], Laine, A.F.[Andrew F.],
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study,
MedImg(40), No. 12, December 2021, pp. 3652-3662.
IEEE DOI 2112
Lung, Computed tomography, Indexes, Shape, Biomedical imaging, Training, Lung CT, emphysema, lung texture BibRef

Guo, Z.Q.[Zhi-Qiang], Xu, L.[Lina], Si, Y.J.[Yu-Juan], Razmjooy, N.[Navid],
Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics,
IJIST(31), No. 4, 2021, pp. 1954-1969.
DOI Link 2112
computer-aided design, convolutional neural network, Haralick texture features, improved Harris Hawks optimizer, lung cancer diagnosis BibRef

Kailasam, M.S.[Manoj Senthil], Thiagarajan, M.[MeeraDevi],
Detection of lung tumor using dual tree complex wavelet transform and co-active adaptive neuro fuzzy inference system classification approach,
IJIST(31), No. 4, 2021, pp. 2032-2046.
DOI Link 2112
CANFIS, computed tomography, DT-CWT, filters, lung tumor, multiresolution BibRef

Jena, S.R.[Sanjukta Rani], George, S.T.[Selvaraj Thomas], Ponraj, D.N.[Deivendran Narain],
Modeling an effectual multi-section You Only Look Once for enhancing lung cancer prediction,
IJIST(31), No. 4, 2021, pp. 2144-2157.
DOI Link 2112
CNN, features, lung nodules, You Only Look Once BibRef

Shi, F.[Feng], Chen, B.J.[Bo-Jiang], Cao, Q.Q.[Qi-Qi], Wei, Y.[Ying], Zhou, Q.[Qing], Zhang, R.[Rui], Zhou, Y.J.[Yao-Jie], Yang, W.J.[Wen-Jie], Wang, X.[Xiang], Fan, R.R.[Rong-Rong], Yang, F.[Fan], Chen, Y.B.[Yan-Bo], Li, W.M.[Wei-Min], Gao, Y.Z.[Yao-Zong], Shen, D.G.[Ding-Gang],
Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images,
MedImg(41), No. 4, April 2022, pp. 771-781.
IEEE DOI 2204
Lung, Computed tomography, Lung cancer, Tumors, Transfer learning, Semisupervised learning, Medical diagnostic imaging, benign-malignant classification BibRef

Manickam, M.[Muruganantham], Siva, R.[Rathinavelayutham], Prabakeran, S.[Saravanan], Geetha, K.[Kannan], Indumathi, V.[Varadharajan], Sethukarasi, T.[Thirumaaran],
Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach,
IJIST(32), No. 3, 2022, pp. 843-856.
DOI Link 2205
African vulture optimization, emphysema, fibrosis, pneumothorax, prediction, pulmonary diseases, SVM BibRef

Ananth, A.D.[Antony Dennis], Palanisamy, C.[Chenniappan],
Extended and optimized deep convolutional neural network-based lung tumor identification in big data,
IJIST(32), No. 3, 2022, pp. 918-934.
DOI Link 2205
big data, classification, CT images, deep learning, feature extraction, lung tumor detection, segmentation BibRef

Hu, J.H.[Jun-Hao], Zhang, C.Y.[Chen-Yang], Zhou, K.[Kang], Gao, S.H.[Sheng-Hua],
Chest X-Ray Diagnostic Quality Assessment: How Much Is Pixel-Wise Supervision Needed?,
MedImg(41), No. 7, July 2022, pp. 1711-1723.
IEEE DOI 2207
Diagnostic radiography, Image segmentation, Semantics, Quality assessment, Lung, X-ray imaging, Annotations, box supervision BibRef

Thirumagal, E.[Egambaram], Saruladha, K.[Krishnamurthy],
Lung cancer classification using exponential mean saturation linear unit activation function in various generative adversarial network models,
IJIST(32), No. 4, 2022, pp. 1414-1428.
DOI Link 2207
activation function, cancer classification, dataset enhancement, GAN, mortality BibRef

Jacob, C.[Chinnu], Menon, G.C.[Gopakumar Chandrasekhara],
Pathological categorization of lung carcinoma from multimodality images using convolutional neural networks,
IJIST(32), No. 5, 2022, pp. 1681-1695.
DOI Link 2209
BRISQUE, computed tomography (CT), convolutional neural networks (CNN), lung cancer, pathological type classification BibRef

Wang, H.F.[Hong-Fei], Yang, P.[Ping], Xu, C.[Chuan], Min, L.[Lei], Wang, S.[Shuai], Xu, B.[Bing],
Lung CT image enhancement based on total variational frame and wavelet transform,
IJIST(32), No. 5, 2022, pp. 1604-1614.
DOI Link 2209
image enhancement, lung CT, noise removal, total variation, wavelet transform BibRef

Thorat, O.[Onkar], Salvi, S.[Siddharth], Dedhia, S.[Shrey], Bhadane, C.[Chetashri], Dongre, D.[Deepika],
Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases,
IJIST(32), No. 5, 2022, pp. 1535-1547.
DOI Link 2209
deep learning, domain adaptation, HRCT scans, interstitial lung diseases, transfer learning BibRef

Sünnetci, K.M.[Kubilay Muhammed], Alkan, A.[Ahmet],
Lung cancer detection by using probabilistic majority voting and optimization techniques,
IJIST(32), No. 6, 2022, pp. 2049-2065.
DOI Link 2212
bag of features, data mining, lung cancer, machine learning, probabilistic majority voting BibRef

Huang, P.[Pan], He, P.[Peng], Tian, S.[Sukun], Ma, M.[Mingrui], Feng, P.[Peng], Xiao, H.[Hualiang], Mercaldo, F.[Francesco], Santone, A.[Antonella], Qin, J.[Jing],
A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images,
MedImg(42), No. 1, January 2023, pp. 15-28.
IEEE DOI 2301
Cancer, Tumors, Adaptation models, Deep learning, Optimization, Measurement, Visualization, Visually interpretable, model fusion, histopathology images BibRef

Spoorthi, B., Mahesh, S.[Shanthi],
Firefly Competitive Swarm Optimization Based Hierarchical Attention Network for Lung Cancer Detection,
IJIG(23), No. 2 2023, pp. 2350017.
DOI Link 2303
BibRef

Balachandran, S.[Sangeetha], Ranganathan, V.[Vidhyapriya],
Semantic context-aware attention UNET for lung cancer segmentation and classification,
IJIST(33), No. 3, 2023, pp. 822-836.
DOI Link 2305
context-aware attention UNET, lung cancer detection, lung nodule segmentation, semantic segmentation BibRef

Pawar, S.P.[Swati P.], Talbar, S.N.[Sanjay N.],
Multi-level deep learning based lung cancer classifier for classification based on tumour-node-metastasis approach,
IJIST(33), No. 3, 2023, pp. 881-894.
DOI Link 2305
computer-aided diagnosis, conditional, generative adversarial network, lung CT scan, tumor-node-metastasis BibRef

Liu, C.X.[Cai-Xia], Xie, W.L.[Wan-Li], Zhao, R.B.[Rui-Bin], Pang, M.Y.[Ming-Yong],
Segmenting lung parenchyma from CT images with gray correlation-based clustering,
IET-IPR(17), No. 6, 2023, pp. 1658-1667.
DOI Link 2305
contour correction, gray correlation-based clustering, image preprocessing, lung segmentation BibRef

Kiran, S.V.[S. Vishwa], Kaur, I.[Inderjeet], Thangaraj, K., Saveetha, V., Grace, R.K.[R. Kingsy], Arulkumar, N.,
Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images,
IJIG(23), No. 3 2023, pp. 2240002.
DOI Link 2306
BibRef

Barbouchi, K.[Khalil], El Hamdi, D.[Dhekra], Elouedi, I.[Ines], Ben Aïcha, T.[Takwa], Echi, A.K.[Afef Kacem], Slim, I.[Ihsen],
A transformer-based deep neural network for detection and classification of lung cancer via PET/CT images,
IJIST(33), No. 4, 2023, pp. 1383-1395.
DOI Link 2307
computed tomography, histology staging, lung cancer, positron emission tomography, TNM staging system, transformers BibRef

Chang, Z.Z.[Zu-Zheng], Rodriguez, D.[Dragan],
Optimized lung cancer detection by amended whale optimizer and rough set theory,
IJIST(33), No. 5, 2023, pp. 1713-1726.
DOI Link 2310
amended whale optimization algorithm, K-means, lung cancer diagnosis, RBF network, rough set theory BibRef

Xie, Y.H.[Ying-Hua], Zhou, Y.T.[Yun-Tong], Wang, C.[Chen], Ma, Y.[Yanshan], Yang, M.[Ming],
Multi-scale feature fusion network with local attention for lung segmentation,
SP:IC(119), 2023, pp. 117042.
Elsevier DOI 2310
Multi-scale, Local attention module, Lung segmentation BibRef

Tyagi, S.[Shweta], Talbar, S.N.[Sanjay N.],
Predicting lung cancer treatment response from CT images using deep learning,
IJIST(33), No. 5, 2023, pp. 1577-1592.
DOI Link 2310
convolutional neural networks, CT scan, deep learning, lung cancer, segmentation, treatment analysis BibRef

Fan, X.C.[Xiao-Chen], Xu, X.[Xin], Feng, J.X.[Jian-Xing], Huang, H.X.[Hai-Xia], Zuo, X.[Xiang], Xu, G.[Guohou], Ma, G.H.[Guang-Hui], Chen, B.[Bin], Wu, J.B.[Jian-Bin], Huang, Y.[Yinhua], Luo, Y.[Yang],
Learnable interpolation and extrapolation network for fuzzy pulmonary lobe segmentation,
IET-IPR(17), No. 11, 2023, pp. 3258-3270.
DOI Link 2310
biomedical imaging, image segmentation, interpolation, learning (artificial intelligence), selected BibRef

Peng, Y.Y.[Yuan-Yuan], Zhang, J.X.[Jia-Xing],
Lung lobe segmentation in computed tomography images based on multi-feature fusion and ensemble learning framework,
IJIST(33), No. 6, 2023, pp. 2088-2099.
DOI Link 2311
CT, ensemble learning framework, lung lobe segmentation, multi-feature fusion BibRef

Xu, X.B.[Xue-Bin], Lei, M.[Meng], Liu, D.H.[De-Hua], Wang, M.[Muyu], Lu, L.B.[Long-Bin],
Lung segmentation in chest X-ray image using multi-interaction feature fusion network,
IET-IPR(17), No. 14, 2023, pp. 4129-4141.
DOI Link 2312
computer vision, convolutional neural nets, image segmentation BibRef

Li, W.[Wei], Liu, G.H.[Guang-Hai], Fan, H.[Haoyi], Li, Z.Y.[Zuo-Yong], Zhang, D.[David],
Self-Supervised Multi-Scale Cropping and Simple Masked Attentive Predicting for Lung CT-Scan Anomaly Detection,
MedImg(43), No. 1, January 2024, pp. 594-607.
IEEE DOI 2401
BibRef

Sivakumar, V., Yogesh, C.K., Vatchala, S., Kaliraj, S.,
An efficient lung image classification and detection using spiral-optimized Gabor filter with convolutional neural network,
IJIST(34), No. 1, 2024, pp. e23013.
DOI Link 2401
convolutional neural networks (CNNs), feature extraction, Gabor filter, lung cancer, spiral optimization algorithm (SOA) BibRef

Chaudhary, M.F.A.[Muhammad F. A.], Gerard, S.E.[Sarah E.], Christensen, G.E.[Gary E.], Cooper, C.B.[Christopher B.], Schroeder, J.D.[Joyce D.], Hoffman, E.A.[Eric A.], Reinhardt, J.M.[Joseph M.],
LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation,
MedImg(43), No. 7, July 2024, pp. 2448-2465.
IEEE DOI 2407
Computed tomography, Lung, Generative adversarial networks, Transformers, Solid modeling, Image synthesis, vision transformers BibRef

Hamdi, D.E.[Dhekra El], Elouedi, I.[Ines], Slim, I.[Ihsen],
Computer-Aided Classification of Cell Lung Cancer Via PET/CT Images Using Convolutional Neural Network,
IJIG(24), No. 4, July 2024, pp. 2450040.
DOI Link 2408
BibRef

Ma, L.[Lei], Wu, H.Q.[Hui-Qun], Samundeeswari, P.,
GoogLeNet-AL: A fully automated adaptive model for lung cancer detection,
PR(155), 2024, pp. 110657.
Elsevier DOI 2408
Lung cancer classification, Deep learning, Convolutional neural networks, GoogLeNet-AL, Adaptive layers, Medical image analysis BibRef

Chen, Y.F.[Yi-Fei], Zou, B.F.[Bin-Feng], Guo, Z.X.[Zhao-Xin], Huang, Y.[Yiyu], Huang, Y.F.[Yi-Fan], Qin, F.[Feiwei], Li, Q.[Qinhai], Wang, C.M.[Chang-Miao],
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation*,
WACV24(7744-7752)
IEEE DOI Code:
WWW Link. 2404
Image segmentation, Fuses, Computed tomography, Pulmonary diseases, Computer network reliability, Medical services, Image recognition and understanding BibRef

Li, G.Y.[Gary Y.], Chen, L.[Li], Zahiri, M.[Mohsen], Balaraju, N.[Naveen], Patil, S.[Shubham], Mehanian, C.[Courosh], Gregory, C.[Cynthia], Gregory, K.[Kenton], Raju, B.[Balasundar], Kruecker, J.[Jochen], Chen, A.[Alvin],
Weakly Semi-supervised Detector-based Video Classification with Temporal Context for Lung Ultrasound,
CVAMD23(2475-2484)
IEEE DOI 2401
BibRef