A Novel Deep Learning-based Whale Optimization Algorithm for Prediction of Breast Cancer

Rana, Poonam; Gupta, Praveen; Sharma, Vineet K.

doi:10.1590/1678-4324-2021200221

Cited by 14 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Propose a new deep architecture based on self-integration to leverage semantic information from annotated images and explore information hidden in unlabeled data [140] Propose an analysis and synthesis model learning method with novel algorithms and search strategies to classify images more effectively [141][142][143][144][145][146][147][148][149][150] Propose a set of training techniques and use image processing techniques to improve the performance of CNN-based models in breast cancer classification [143,[151][152][153][154][155][156][157] Deep residual network (ResNet) Present a deep neural network which performs representation learning and cell nuclei recognition in an end-to-end manner [158] Propose an automatic multiclassification method for breast cancer histopathological images based on metastasis learning [159] Present a method that employs a convolutional neural network for detecting tumor on entire-slide images [59,130,136,160] Propose a breast cancer multiclassification method using a proposed deep learning model [106,113,137,[161][162][163][164][165][166][167][168] Thus, through research and analysis, we found that the classifier proposed by [110] first predicts the class label of each input patch by OPOD technique and then predicts the whole-image label by APOD technique. At the same time, the number o...…”

Section: Model Strategy Advantages Publicationmentioning

confidence: 99%

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Zhao

Zhang

et al. 2022

Micromachines

View full text Add to dashboard Cite

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

show abstract

Section: Model Strategy Advantages Publicationmentioning

confidence: 99%

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Zhao

Zhang

et al. 2022

Micromachines

View full text Add to dashboard Cite

show abstract

“…However, those algorithms have not yet been applied to optimize the hyperparameters of the classification model. On the other hand, the whale optimization algorithm (WOA) [21] has been applied in some studies to optimize the hyperparameters of the CNN model, as reported in [22,23]. Both of the aforementioned studies demonstrate how WOA can significantly improve CNN's classification performance.…”

Section: Introductionmentioning

confidence: 99%

Optimized One-Dimension Convolutional Neural Network for Seizure Classification from EEG Signal based on Whale Optimization Algorithm

2023

IJIES

View full text Add to dashboard Cite

Epilepsy is a chronic disorder that causes sudden, recurring seizures and early detection of seizures is needed for prompt treatment to reduce the higher risk. An electroencephalogram (EEG) can detect epilepsy based on traces of electrical activity and wave patterns in the brain. However, analyzing EEG signals takes a long time and is operated by neuroscientists. In this paper, we propose automatic seizure detection using a one-dimension convolutional neural network (1D CNN) and the approach of whale optimization algorithm (WOA). The EEG signal is trimmed every three seconds, and features are extracted using discrete wavelet transform (DWT). The WOA approach was used to optimize the number of layers and neurons in 1D CNN. The experimental results show that the proposed model can improve CNN's performance in detecting seizures with an accuracy of 99.76%, respectively. The proposed method is suitable for the children's hospital bostonmassachusetts institute of technology (CHB-MIT) dataset.

show abstract

Crown Prediction of Hot‐Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm

Meng

Ding

Dong

et al. 2023

steel research int.

View full text Add to dashboard Cite

Massive amounts of data are generated during hot strip production in the hot‐rolling metal forming process; the resultant dataset is sufficient for model learning in strip steel crown prediction. However, the data of high‐grade nonoriented silicon steel are limited, and the rolling process parameters differ, resulting in poor crown prediction. Herein, a model based on the whale optimization algorithm and transfer learning to predict the crowns of hot‐rolled high‐grade nonoriented silicon strip steel is presented. The model is composed of convolutional and linear layers. The whale optimization algorithm is used to optimize the hyperparameters and obtain an optimal model during pretraining. The model is then fine tuned based on insufficient silicon steel data to achieve model migration. The application results show that the correlation coefficient reaches 0.993, which is the highest prediction accuracy among the comparison models. Furthermore, the root mean square error is 1.14 μm, and the hit rate within 4.0 μm of the crown deviation reaches 99.502%. In addition, the influences of four parameters on the crown of the silicon steel strip are studied based on the response surfaces. The results indicate that the proposed model can efficiently predict silicon steel strip crowns.

show abstract

A Novel Deep Learning-based Whale Optimization Algorithm for Prediction of Breast Cancer

Cited by 14 publications

References 18 publications

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Optimized One-Dimension Convolutional Neural Network for Seizure Classification from EEG Signal based on Whale Optimization Algorithm

Crown Prediction of Hot‐Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm

Contact Info

Product

Resources

About