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Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

Blockchain technology (BCT) has emerged as a powerful tool for enhancing transparency and trust. However, the relationship between the benefits of BCT and agri-food supply chain performance (AFSCperf) remains underexplored. Therefore, the current study investigates the influence of BCT on AFSCperf and sustainability issues.

Through a comprehensive literature review, various benefits of BCT are identified. Subsequently, a research framework is proposed based on data collected from questionnaire surveys and personal visits to professionals in the agri-food industry. The proposed framework is validated using partial least square structural equation modelling (PLS-SEM).

The findings reveal that BCT positively impacts AFSCperf by improving traceability, transparency, food safety and quality, immutability and trust. Additionally, BCT adoption enhances stakeholder collaboration, provides a decentralised network, improves data accessibility and yields a better return on investment, resulting in the overall improvement in AFSCperf and socio-economic sustainability.

This study offers valuable practical insights for practitioners and academicians, establishing empirical links between the benefits of BCT and AFSCperf and providing a deeper understanding of BCT adoption.

Stakeholders, managers, policymakers and technology providers can leverage these findings to optimise the benefits of BCT in enhancing AFSCperf. Moreover, it utilises rigorous theoretical and empirical approaches, drawing on a multidisciplinary perspective encompassing food operations and supply chain literature, public policy, information technology, strategy, organisational theory and sustainability.