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

Goncalves, Cristiane Sales; Souza, Jefferson R.; Fernandes, Henrique

doi:10.1016/j.compbiomed.2021.105205

Cited by 64 publications

(20 citation statements)

References 8 publications

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“…However, prior to doing that, the image must be pre-processed so that its dimensions match those of the layer. In the CNN architecture VGG-16 algorithm [14] employed in the wood classification system, the parameter of appropriateness is a square-shaped image with dimensions of 224 x 224 x 3 pixels. Based on Eq.…”

Section: ) Convolutional Neural Network (Cnn) Methodsmentioning

confidence: 99%

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Murti¹,

Setianingsih²,

Kusumawardhani³

et al. 2022

IJACSA

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Cedarwood is one of the most sought-after materials since it can be used to create a wide variety of household appliances. Other than its unique aroma, the product's quality is the most important selling attribute. Fiber patterns allow for a qualitative categorization of this wood. Traditionally, workers in the wood-processing business have relied solely on their eyesight to sort materials into several categories. As a result, there will be discrepancies in precision and efficiency, which will hurt the reputation of the regional wood sector. The answer to this issue is machine learning. In this study, we compare the performance of two different cedarwood quality classification systems where both systems use different machine learning methods namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN). Each system will be sent images captured with a Logitech Brio 4K equipped with a joystick and ultrasonic sensors, labeled as belonging to one of five cedar classes (A, B, C, D, or E). In the initial method to learn the wood's pattern and texture, the Histogram of Oriented Gradient (HOG) will be used to identify the material. Meanwhile, the classification method uses a Support Vector Machine (SVM) which will be compared to find the best accuracy and time computation. The first system's experiment achieves 90 percent accuracy with a computation time of 1.40 seconds. For the second, we use a Convolutional Neural Network, a deep learning technique, to classify cedarwood (CNN). Extraction of features occurs in the convolution, activation, and pooling layers. Experimental results demonstrated a considerable enhancement, with an accuracy of 97% and a prediction speed of 0.56 seconds.

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Section: ) Convolutional Neural Network (Cnn) Methodsmentioning

confidence: 99%

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Murti¹,

Setianingsih²,

Kusumawardhani³

et al. 2022

IJACSA

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“…Caroline et al [ 62 ] proposed hybrid optimization techniques based on particle swarm and genetic optimization to discover optimal hyperparameters for the fully connected layers. This study improves the F1-score by 0.90 for all three networks: ResNet-50, VGG-16, and DenseNet-201.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

et al. 2022

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.

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“…The idea of DCNNs depends upon the fact that these networks signify the advancement in many image-recognition situations [ 23 ]. Furthermore, we want to utilize the essential capability of CNNs to extract features automatically with increasing meaning [ 24 , 25 ]. The state of the art presents different CNNs models, CiFarNet [ 26 ], AlexNet [ 27 ], GoogLeNet [ 28 ], ResNet [ 29 ], VGG16, and VGG 19 [ 30 ].…”

Section: Deep Convolutional Neural Network (Dcnns)mentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

Alyami

Sadad

Rehman

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.

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

Cited by 64 publications

References 8 publications

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

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