Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach

Muñoz-Aseguinolaza, Unai; Fernandez-Iriondo, Izaro; Rodríguez-Moreno, Itsaso; Aginako, Naiara; Sierra, Basilio

doi:10.1016/j.heliyon.2023.e21203

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Moreover, image samples were visualized, presenting original images alongside the sample prediction result [44]. Then, global variables were adjusted based on observed results, allowing for further optimization and exploration of model configurations and re-train the model [45]. Finally, all results will be compiled to assess and evaluate which architecture provides a better performance.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Prianes,

Fortuno,

Onesa

et al. 2024

IJACSA

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In the era of rapid technological advancement, the integration of cutting-edge technologies plays a pivotal role in enhancing the efficiency and responsiveness of critical systems. iRESPOND, a real-time Geospatial Information and Alert System, stands at the forefront of such innovations, facilitating timely and informed decision-making in dynamic environments. As the demand for accurate and swift responses, the role of CNN models in iRESPOND becomes significant. The study focuses on seven prominent CNN architectures, namely EfficientNet (B0, B7, V2B0, and V2L), InceptionV3, ResNet50, and VGG19 and with the integration of different optimizers and learning rates. The methodology employed a strategic implementation of looping during the training phase. This iterative approach is designed to systematically re-train the CNN models, emphasizing identifying the most suitable architecture among the seven considered variants. The primary objective is to discern the optimal architecture and fine-tune critical parameters, explicitly targeting the optimizer and learning rate values. The differential impact of each model on the system's ability is to discern patterns and anomalies in the image datasets. ResNet50 exhibited robust performance showcasing suitability for real-time processing in dynamic environments with a better accuracy result of 95.02%. However, the EfficientNetV2B0 model, characterized by its advancements in network scaling, presented promising results with a lower loss of 0.187. Generally, the findings not only contribute valuable insights into the optimal selection of architectures for iRESPOND but also highlight the importance of fine-tuning hyperparameters through an iterative training approach, which paves the way for the continued enhancement of iRESPOND as an adaptive system.

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Section: Methodsmentioning

confidence: 99%

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Prianes,

Fortuno,

Onesa

et al. 2024

IJACSA

View full text Add to dashboard Cite

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Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

Zhang,

Aamir,

Guan

et al. 2024

J Cloud Comp

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The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.

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Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach

Cited by 2 publications

References 35 publications

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

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