CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images

The early detection of breast cancer is a vital factor when it comes to improving cure and recovery rates in patients. Among such early detection factors, one finds thermography, an imaging technique that demonstrates good potential as an early detection method. Convolutional neural networks (CNNs)...

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Bibliographic Details
Published in:Computers in biology and medicine 2022-03, Vol.142, p.105205-105205, Article 105205
Main Authors: Gonçalves, Caroline Barcelos, Souza, Jefferson R., Fernandes, Henrique
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Bio-inspired optimization
Brain cancer
Breast cancer
Breast Neoplasms - diagnostic imaging
Classification
CNN
Datasets
Female
Genetic algorithms
Humans
Image classification
Imaging techniques
Infrared imagery
Infrared images
Mammography
Medical imaging
Neural networks
Neural Networks, Computer
Optimization
Particle swarm optimization
Patients
PSO
Skin cancer
Thermography
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Summary:The early detection of breast cancer is a vital factor when it comes to improving cure and recovery rates in patients. Among such early detection factors, one finds thermography, an imaging technique that demonstrates good potential as an early detection method. Convolutional neural networks (CNNs) are widely used in image classification tasks, but finding good hyperparameters and architectures for these is not a simple task. In this study, we use two bio-inspired optimization techniques, genetic algorithm and particle swarm optimization to find good hyperparameters and architectures for the fully connected layers of three state of the art CNNs: VGG-16, ResNet-50 and DenseNet-201. Through use of optimization techniques, we obtained F1-score results above 0.90 for all three networks, an improvement from 0.66 of the F1-score to 0.92 of the F1-score for the VGG-16. Moreover, we were also able to improve the ResNet-50 from 0.83 of the F1-score to 0.90 of the F1-score for the test data, when compared to previously published studies. •Use of GA for CNNs optimization hyperparameters and fully connected layers.•Use of PSO for CNNs optimization.•Optimized CNN architectures for breast cancer detection.•Infrared imagery to support computer-aided diagnosis of breast cancer detection.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2021.105205