Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients

Ghashghaei, Sara; Wood, David A.; Sadatshojaei, Erfan; Jalilpoor, Mansooreh

doi:10.1007/s42979-022-01642-8

Cited by 1 publication

(1 citation statement)

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“…Deep learning methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been widely used in medical imaging applications, due to their ability to detect and classify patterns in data more accurately and efficiently than traditional methods [ 4 , 5 ]. This is especially important for medical images, where time is of essence [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

Gugulothu

Balaji

2023

Multimed Tools Appl

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Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. Using 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 the complexity of the surrounding environment. Here, we proposed lung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce a chaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate an improved Fish Bee (IFB) algorithm for feature extraction and selection. Third, we develop a hybrid classifier i.e. hybrid differential evolution-based neural network (HDE-NN) for tumor prediction and classification. Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

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Section: Introductionmentioning

confidence: 99%