An improved CNN-based architecture for automatic lung nodule classification

Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient’s chance of surviving is mostly done in two phases: screening through CT scan imaging modality and, more importantly...

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Veröffentlicht in:Medical & biological engineering & computing 2022-07, Vol.60 (7), p.1977-1986
Hauptverfasser: Mahmood, Sozan Abdullah, Ahmed, Hunar Abubakir
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container_end_page 1986
container_issue 7
container_start_page 1977
container_title Medical & biological engineering & computing
container_volume 60
creator Mahmood, Sozan Abdullah
Ahmed, Hunar Abubakir
description Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient’s chance of surviving is mostly done in two phases: screening through CT scan imaging modality and, more importantly the medical expert’s reading of the scan, which is a time-consuming task and is vulnerable to errors. It is difficult to differentiate between malignant and benign nodules and biopsies are highly invasive, and patients with benign nodules may undergo unnecessary procedures. In this study, we propose a CNN-based computer-aided diagnosis system to automatically classify pulmonary nodules into benign or malignant. The proposed network architecture is based on AlexNet architecture that experiments with several types of layer ordering, hyperparameters, and functions for the various sides of the network. To build a well-trained model, several pre-processing steps are applied to the entire dataset, for instance segmentation, normalization, and zero centering. Finally, the proposed system obtained results with 98.7% accuracy, 98.6% sensitivity, and 98.9% specificity. The proposed model achieved superior performance compared to the AlexNet. The modifications in the original AlexNet is done to get a reasonable structure that has high nodule analysis sensitivity. Graphical abstract
doi_str_mv 10.1007/s11517-022-02578-0
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source SpringerNature Journals; EBSCOhost Business Source Complete
subjects Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Biopsy
Computed tomography
Computer Applications
Computer architecture
Deep learning
Diagnosis
Human Physiology
Image segmentation
Imaging
Invasiveness
Lung cancer
Lung nodules
Medical diagnosis
Nodules
Original Article
Patients
Radiology
Sensitivity analysis
title An improved CNN-based architecture for automatic lung nodule classification
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