Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach i...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Biomimetics (Basel, Switzerland) Ročník 8; číslo 2; s. 163
Hlavní autoři:	Alhussan, Amel Ali, Eid, Marwa M., Towfek, S. K., Khafaga, Doaa Sami
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 17.04.2023 MDPI
Témata:	Accuracy Algorithms Alzheimer's disease Automation Brain cancer Breast cancer Classification Deep learning Diagnosis dipper throated optimization algorithm Feature selection Fuzzy logic Lung cancer Machine learning Mammography Medical imaging Medical research Medical screening Neural networks Optimization particle swarm optimization algorithm Semantics Statistical analysis Transfer learning Ultrasonic imaging United States deep learning breast cancer particle swarm optimization algorithm feature selection dipper throated optimization algorithm
ISSN:	2313-7673, 2313-7673
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2313-7673 2313-7673
DOI:	10.3390/biomimetics8020163