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Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction
2022
Intelligent Automation and Soft Computing
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Hidden layer 2 performs the patient Data Classification based on Czekanowski's dice similarity coefficient with the selected relevant features from the previous layer to predict lung cancer. ...
The experimental results reveal that the TPFMDANN-LSTM technique performs better with a 6% improvement in prediction accuracy, 36% reduction of false positives, and 16% faster prediction time for lung ...
The selected relevant features are used for classification to predict lung disease. The process of feature selection and classification is explained in the following subsections. ...
doi:10.32604/iasc.2022.019546
fatcat:nolgfwsiijbcfnjgu6wui2nlqe
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics
[article]
2019
arXiv
pre-print
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Early detection of lung cancer is essential in reducing mortality. ...
Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. ...
This research was conducted with the support from Intramural Research Program, National Institute on Aging, NIH. This study was supported in part by a UO1 CA196405 to Massion. ...
arXiv:1902.08236v1
fatcat:amvo2y52zffvdcfbzxncbtznx4
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
2021
IEEE Access
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In such an environment, medical diagnostic tools need to be explainable, predictable, understandable, and transparent. ...
INTRODUCTION Lung cancer is considered the second most common cancer type in both men and women [1] . ...
doi:10.1109/access.2021.3089704
fatcat:dydjkzvxtbeixbmopzn2wvdaye
The application of artificial intelligence and radiomics in lung cancer
2020
Precision Clinical Medicine
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With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. ...
In this study, we gave a brief review of the current application of AI and radiomics for precision medical management in lung cancer. ...
For example, Huang et al. performed the LASSO Cox-regression model to select the most valuable features for the prediction of prognosis of early-stage non-small cell lung cancer 64 . ...
doi:10.1093/pcmedi/pbaa028
pmid:35694416
pmcid:PMC8982538
fatcat:gpjll7iwavgppgfmcm4qiwnl5e
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
[article]
2021
arXiv
pre-print
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In addition, we show that training the R2U3D model with a smaller number of CT scans, i.e., 100 scans, without applying data augmentation achieves an outstanding result in terms of Soft Dice Similarity ...
3D lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. ...
In such an environment, medical diagnostic tools need to be explainable, predictable, understandable, and transparent. ...
arXiv:2105.02290v1
fatcat:mquv4kpmlnho5pzc2uppn6jfeu
The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review
2018
Translational Cancer Research
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computer vision and machine learning techniques to be applied effectively. ...
To facilitate the detection machine learning and deep learning methods are increasingly investigated with the aim of improving patient diagnosis, treatment options and outcomes. ...
(13) introduced a novel radiomics approach by fusing FDG-PET scans with MRI imaging to accurately predict the occurrence of lung metastasis in patients with soft tissue sarcoma (STS). ...
doi:10.21037/tcr.2018.05.02
fatcat:qfbyqs45nzayplkszkt3wvvw2q
Early Detection of Lung Carcinoma Using Machine Learning
2021
Intelligent Automation and Soft Computing
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Semantics preprocessing of a lung cancer training set is performed with least entropy, and then translation, aggregation, and navigation based methodologies are applied for identifying the disease at its ...
Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. ...
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study. ...
doi:10.32604/iasc.2021.016242
fatcat:xmujbb7xc5gczikboguolir2ta
Gene based Disease Prediction using Pattern Similarity based Classification
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Our experimental results show that proposed semi supervised classifier performance improved in accuracy percentage. ...
For addressing the fundamental harms which helps to diagnosis and discovery gene expression data along with diseases classification is included. ...
And also provides the currently emerging areas in bioinformatics where feature selection can be applied and is able to achieve better performance. ...
doi:10.35940/ijitee.k2524.0981119
fatcat:ghjrlzpz6rewhbwtfbsat4k3ua
Cancer prediction using graph-based gene selection and explainable classifier
2022
Finnish Journal of eHealth and eWelfare
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In this study, an efficient and effective model is developed for gene selection and cancer prediction. ...
In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent ...
Applied Soft Computing. 2021;100:106994. https://doi.org/10.1016/j.asoc.2020.106994 [4] Babu P SA, Annavarapu CSR, Dara S. ...
doi:10.23996/fjhw.111772
fatcat:paqhmluzuzcbhc2suexkmofmva
Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19
2020
Machine Vision and Applications
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, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. ...
Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. ...
[73] reviewed the computer-aided detection systems that improve the early diagnosis of lung cancer. ...
doi:10.1007/s00138-020-01101-5
pmid:32834523
pmcid:PMC7386599
fatcat:tkkylrptc5hkpoj52hjs3kuttu
Deep Separable Convolution Network for Prediction of Lung Diseases from X-rays
2022
International Journal of Advanced Computer Science and Applications
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Used A deep separable convolution network (DSCN) was in the created system to predict the class of lung cancer, and Modified Butterfly Optimization Algorithm (MBOA) applied for the feature selection procedure ...
Accurate diagnosis of lung cancer has been critical, and image segmentation and deep learning (DL) techniques have made it easier for medical people. ...
In order to improve cancer detection performance, the phases of background segmentation, feature set extraction, feature optimization, and DSCNN-based LC classification are used. ...
doi:10.14569/ijacsa.2022.0130662
fatcat:22ywocvms5g7fkejgb7qkz3bwm
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
2018
Cancer Genomics & Proteomics
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Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading ...
Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. ...
Cancer Foundation, the Research Institute of Oncology and Hematology Summer student research fund, and CancerCare Manitoba Foundation (CCMF). ...
doi:10.21873/cgp.20063
pmid:29275361
pmcid:PMC5822181
fatcat:o6l764hssnh2nbu4fm6b52f3mi
The detection of lung cancer using massive artificial neural network based on soft tissue technique
2020
BMC Medical Informatics and Decision Making
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A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and specificity. ...
However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtle nodules using x-ray images. ...
But lung nodule detection accuracy was not improved [18] . Feng Li et al. [19, 20] detects small lung cancers in x-ray image for false positive reduction. ...
doi:10.1186/s12911-020-01220-z
pmid:33129343
fatcat:4rog3ykncrcvpfc456nlj2zyju
Coarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
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Our approach is validated comprehensively in colorectal polyp detection and lung nodule detection CAD systems, as the top two deadly cancers, using hospital scale, multi-site clinical datasets. ...
Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. ...
Image interpretation based cancer detection via 3D computer tomography has emerged as a common clinical practice, and many computer-aided detection tools for enhancing radiologists' diagnostic performance ...
doi:10.1145/2063576.2064004
dblp:conf/cikm/LuLYYH11
fatcat:74nsydvy7vbyxbk3kaoejjgjoi
Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges
2020
Korean Journal of Radiology
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Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in ...
However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding ...
CT images reconstructed with soft kernel (A) and sharp kernel (B) from single scan of patient with lung nodule showing different image textures, which may cause variability in radiomic features of lung ...
doi:10.3348/kjr.2019.0821
pmid:32323497
pmcid:PMC7183830
fatcat:a4ejphwn5jbr7mw7vtmyedgegy
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