2022
DOI: 10.21203/rs.3.rs-1190013/v1
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An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Abstract: Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. The use of hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans.Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and th… Show more

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Cited by 4 publications
(21 citation statements)
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“…The diagnostic performance of the proposed RN-PDMP method and existing methods 3D-CNN [1] and LNDC-HDL [2] are tested under the complexity, accuracy, precision and recall factors. The corresponding formulaeof these measures are as given below.…”
Section: Results and Analysismentioning
confidence: 99%
See 3 more Smart Citations
“…The diagnostic performance of the proposed RN-PDMP method and existing methods 3D-CNN [1] and LNDC-HDL [2] are tested under the complexity, accuracy, precision and recall factors. The corresponding formulaeof these measures are as given below.…”
Section: Results and Analysismentioning
confidence: 99%
“…Experimental assessment of proposed, RN-PDMP method and two existing methods 3D-CNN [1] and LNDC-HDL [2] Simulations were performed in Parametric metrics considered for the evaluation of lung cancer detection consists of complexity, disease detection accuracy, precision and recall. Each method has been tested individually over 1000 images of testing set involved that 400 cases diagnosed as malignant, 100 cases diagnosed with benign and 500 cases classified as normal cases to evaluate all performance metrics.…”
Section: Methodsmentioning
confidence: 99%
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“…Gugulothu et al 31 recommended the diagnostic process based on CNN deep transmission and extreme learning machine (ELM), which combines the synergistic effect of both methods to manage the classification of naked dead nodes. DTCNN was the first to adopt high beard characteristics in an already trained image‐net database.…”
Section: Problem Methodology and System Modelmentioning
confidence: 99%