Sage Journals: Discover world-class research

Abstract

Nowadays, breast cancer is a very dangerous sickness among women though well-timed identification relatively enhances the rate of survival. To win over the breast tumor, an effective diagnosis mechanism is very necessary. The removal of tumor cells from the affected area stay as challenging. Classifying the breast tumor is very challenging for pathologists because of the tumor cell's heterogeneous nature. Hence, it is important to adopt an efficient breast tumor classification architecture employing a deep learning strategy. It supports the premature detection of breast cancer and proper treatments that help to prolong the chances of patients’ survival. Initially, the segmentation procedure is executed in the input images using an Adaptive U-Net Generative Adversarial Network (AUNet-GAN). Moreover, the variables of AUNet-GAN are tuned employing the Enhanced Golf Optimization Algorithm (EGOA). The major principle of segmentation is to change its illustration into a meaningful format of the image. Further, the segmented images are fed to the Feature Fusion-based Hybrid Network (FFHNet)-aided classification of the breast cancer model. This classification process is utilized to attain better benign and malignant. Later, several experimental observations are executed to estimate the efficacy rate of the designed method over the baseline models and also diverse efficacy metrics like accuracy, False Negative Rate (FNR), Prevalence Threshold (PT), dice coefficient, etc. The accuracy of the offered approach for datasets 1 and 2 is 94.30 and 95.44. Accordingly, the accuracy values of the existing works like CNN, ResNet, RNN, and VGG16 for dataset 1 are 89.33, 91.70, 91.70, and 90.99 and also for dataset 2 is 89.54, 82.91, 90.99, and 92.75. Thus, the empirical outcomes of this study help to provide proper treatment for breast cancer and the usage of classifiers efficiently improves the accuracy and strength of detection for breast cancer models.

Keywords

Breast cancer segmentation and classification adaptive u-Net with generative adversarial network enhanced golf optimization algorithm feature fusion based hybrid network accuracy dice coefficient

Get full access to this article

View all access options for this article.

References

Patel

Chaurasia

Mahadeva

, et al. GARL-Net: graph based adaptive regularized learning deep network for breast cancer classification. IEEE Access 2023; 11: 9095–9112.

Zheng

Lin

Gao

, et al. Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access 2020; 8: 96946–96954.

. Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 2019; 7: 21400–21408.

Routray

Rout

Sahu

, et al. Ensemble learning with symbiotic organism search optimization algorithm for breast cancer classification and risk identification of other organs on histopathological images. IEEE Access 2023; 11: 110544–110557.

Rajpal

Agarwal

Kumar

, et al. Triphasic DeepBRCA-A deep learning-based framework for identification of biomarkers for breast cancer stratification. IEEE Access 2021; 9: 103347–103364.

Aziz

Munir

Raza

, et al. IVNet: transfer learning based diagnosis of breast cancer grading using histopathological images of infected cells. IEEE Access 2023; 11: 127880–127894.

Peta

Koppu

. Enhancing breast cancer classification in histopathological images through federated learning framework. IEEE Access 2023; 11: 61866–61880.

Saber

Sakr

Abo-Seida

, et al. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 2021; 9: 71194–71209.

Malebary

Hashmi

. Automated breast mass classification system using deep learning and ensemble learning in digital mammogram. IEEE Access 2021; 9: 55312–55328.

10.

Tan

Tinh

Lam

, et al. A transfer learning approach to breast cancer classification in a federated learning framework. IEEE Access 2023; 11: 27462–27476.

11.

Zhang

Zou

Zhou

, et al. Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access 2018; 6: 28936–28944.

12.

Ahmad

Akram

Jaffar

, et al. Breast cancer detection using deep learning: an investigation using the DDSM dataset and a customized AlexNet and support vector machine. IEEE Access 2023; 11: 108386–108397.

13.

Subramanian

Cho

Sathishkumar

, et al. Multiple types of cancer classification using CT/MRI images based on learning without forgetting powered deep learning models. IEEE Access 2023; 11: 10336–10354.

14.

Kim

C-M

Park

Hong

. Breast mass classification using eLFA algorithm based on CRNN deep learning model. IEEE Access 2020; 8: 197312–197323.

15.

Yari

Nguyen

. Deep learning applied for histological diagnosis of breast cancer. IEEE Access 2020; 8: 162432–162448.

16.

Khan

Raza

Waheed

, et al. MSF-Model: multi-scale feature fusion-based domain adaptive model for breast cancer classification of histopathology images. IEEE Access 2022; 10: 122530–122547.

17.

Anas

Haq

Husnain

, et al. Advancing breast cancer detection: enhancing YOLOv5 network for accurate classification in mammogram images. IEEE Access 2024; 12: 16474–16488.

18.

Anastasiadi

Lianos

Ignatiadou

, et al. Breast cancer in young women: an overview. Updates in Surgery 2017; 69: 313–317.

19.

Guo

Xie

Wan

, et al. Bingxin Li and Yongqiang Yao “A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis”. Open Life Sciences 2022; 17: 1600–1611.

20.

Soulami

Kaabouch

Saidi

, et al. Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. Biomed Signal Process Control 2021; 66: 102481.

21.

Hirra

Ahmad

Hussainm

, et al. Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access 2021; 9: 24273–24287.

22.

Zhang

Zheng

, et al. Deep learning based analysis of breast cancer using advanced ensemble classifier and linear discriminant analysis. IEEE Access 2020; 8: 120208–120217.

23.

Arshad

Masood

Mahmood

, et al. Cancer unveiled: a deep dive into breast tumor detection using cutting-edge deep learning models. IEEE Access 2023; 11: 133804–133824.

24.

Aljuaid

Alturki

Alsubaie

, et al. Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning. Comput Methods and Progr in Biomed 2022; 223: 106951.

25.

Kumari

Ghosh

. A magnification-independent method for breast cancer classification using transfer learning. Healthcare Anal 2023; 3: 100207.

26.

Alotaibi

Aljouie

Alluhaidan

, et al. Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images. Heliyon 2023; 9: 22406.

27.

Ahmed

Ouarda

Ben Amar

. Enhanced computer-aided diagnosis model on ultrasound images through transfer learning and data augmentation techniques for an accurate breast tumors classification. Procedia Computer Science 2023; 225: 3938–3947.

28.

Mahesh

Vinoth Kumar

Dhilip Kumar

, et al. The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification. Healthcare Anal 2023; 4: 100247.

29.

Montazeri

Niknam

Aghaei

, et al. Golf optimization algorithm: a new game-based metaheuristic algorithm and its application to energy commitment problem considering resilience. Biomimetics 2023; 8: 386.

30.

Bousias Alexakis

Armenakis

. Evaluation of UNet and UNet++ architectures in high resolution image change detection applications. Int Arch of the Photogrammetry, Remote Sens Spatial Inf Sci 2020; 43: 1507–1514.

31.

Zeng

Zhou

. Photo-realistic face age progression/regression using a single generative adversarial network. Neurocomputing 2019; 366: 295–304.

32.

Qanbar

Tasdemir

. Detection of malaria diseases with residual attention network. Int J of Intell Syst Appl Eng 2019; 7: 238–244.

33.

Tammina

. Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int J of Sci Res Publ (IJSRP) 2019; 9: 143–150.

34.

Azizi

Baghalzadeh Shishehgarkhaneh

Basiri

, et al. Squid Game Optimizer (SGO): a novel metaheuristic algorithm. Scic Rep 2023; 13: 5373.

35.

Dehghani

Trojovský

. Teamwork optimization algorithm: A new optimization approach for function minimization/maximization. Sensors 2021; 21: 4567.

36.

Al-Baik

Alomari

Alssayed

, et al. Pufferfish optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Biomimetics 2024; 9: 65.

37.

Heenaye-Mamode Khan

Boodoo-Jahangeer

Dullull

, et al. Multi-class classification of breast cancer abnormalities using deep convolutional neural network (CNN). PloS One 2021; 16:e0256500.

38.

Jiang

Chen

Zhang

, et al. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS One 2019; 14: e0214587.

39.

Anbalagan

Balasubramanian

. HBO-GMRNN: honey badger optimization based gain modulated recurrent neural network for classification of breast cancer. Biomed Signal Process and Control 2024; 91: 105910.

40.

Zang

Liang

, et al. Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet. Sci Rep 2023; 13: 12779.

41.

Sun

Yuan

, et al. BGRD-TransUNet: A Novel TransUNet-based Model for Ultrasound Breast Lesion Segmentation. IEEE Access 2024; 12: 31182–31196.

Breast cancer image analysis utilizing feature fusion-based hybrid classification network influenced by adaptive u-net with generative adversarial segmentation network

Abstract

Keywords

Get full access to this article

References