Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator




Julkaisun tekijät: Liu Haixia, Cui Guozhong, Luo Yi, Guo Yajie, Zhao Lianli, Wang Yueheng, Subasi Abdulhamit, Dogan Sengul, Tuncer Turker

Kustantaja: DOVE MEDICAL PRESS LTD

Julkaisuvuosi: 2022

Journal: International Journal of General Medicine

Tietokannassa oleva lehden nimi: INTERNATIONAL JOURNAL OF GENERAL MEDICINE

Lehden akronyymi: INT J GEN MED

Volyymi: 15

Sivujen määrä: 12

eISSN: 1178-7074

DOI: http://dx.doi.org/10.2147/IJGM.S347491

Verkko-osoite: https://www.dovepress.com/artificial-intelligence-based-breast-cancer-diagnosis-using-ultrasound-peer-reviewed-fulltext-article-IJGM

Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/175021508


Tiivistelmä

Purpose

Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS).

Patients and Methods

This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN).

Results

The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal.

Conclusion

The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.


Ladattava julkaisu

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2022-12-05 at 14:29