Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. Traditional techniques may also struggle with combinatorial optimization problems when applied to a prominent feature space. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quantum Computing based Quadratic unconstrained binary optimization (QC-FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

Retracted: Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer

[This retracts the article DOI: 10.1155/2022/3947434.].

Application effect of low-dose spiral CT on pulmonary nodules and its diagnostic value for benign and malignant nodules

OBJECTIVE

This study was designed to determine the application effect of low-dose computed tomography (LDCT) on detecting pulmonary nodules (PNs) and its diagnostic value for benign and malignant pulmonary nodules.

METHODS

Data of 432 patients with PNs admitted to Julu County Hospital between March 2018 and June 2021 in were collected and analysed retrospectively. All patients underwent LDCT and conventional-dose spiral computed tomography (CT). The detection rate and image characteristics of the two methods were compared, and the image quality and radiation dose of the two diagnostic methods were also compared.

RESULTS

No significant difference was found between LDCT and conventional-dose spiral CT in the detection rate of lung cancer (P>0.05). The area under the curve of conventional-dose CT was 0.932, with a specificity and sensitivity of 93.87% and 92.45%, and the area under the curve of LDCT was 0.902, with a specificity and sensitivity of 90.80% and 89.62%. The radiation dose consumed during LDCT was greatly less than that consumed by conventional-dose CT (P<0.05). Additionally, the two methods were not different in CT image quality and superior vena cava artifact (P>0.05). No notable difference was found between LDCT and conventional-dose CT in terms of the diagnosis rate of PNs in vascular aggregation sign, pleural indentation sign, lobulation sign and spiculation sign.

CONCLUSION

LDCT can clearly show the typical images of early lung cancer, with less effective radiation dose, and can thus contribute to a high detection rate, so it is worth popularizing.