An Effective Breast Cancer Classification System Using Multiple Feature Extraction Techniques with Multi-scale Attention-Based Feature Fusion Model

Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H: A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEe Access. 9:71194-209, 2021

Hirra I, Ahmad M, Hussain A, Ashraf MU, Saeed IA, Qadri SF, Alghamdi AM, Alfakeeh AS: Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access. 9:24273-87, 2021

Liu M, Hu L, Tang Y, Wang C, He Y, Zeng C, Lin K, He Z, Huo W: A deep learning method for breast cancer classification in the pathology images. IEEE Journal of Biomedical and Health Informatics. 26(10):5025-32, 2022

Yari Y, Nguyen TV, Nguyen HT: Deep learning applied for histological diagnosis of breast cancer. IEEE Access. 8:162432-48, 2020

Zhou Y, Zhang C, Gao S: Breast cancer classification from histopathological images using resolution adaptive network. IEEE Access. 10:35977-91, 2022

Mashudi NA, Rossli SA, Ahmad N, Noor NM: Comparison on some machine learning techniques in breast cancer classification. In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) 499–504, 2021. IEEE.

Naseem U, Rashid J, Ali L, Kim J, Haq QE, Awan MJ, Imran M: An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers. Ieee Access. 10:78242-52, 2022

Rahman AS, Belhaouari SB, Bouzerdoum A, Baali H, Alam T, Eldaraa AM: Breast mass tumor classification using deep learning. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) 271–276, 2020. IEEE.

Zheng J, Lin D, Gao Z, Wang S, He M, Fan J: Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access. 8:96946-54, 2020

Ahmad J, Akram S, Jaffar A, Rashid M, Bhatti SM. Breast Cancer Detection Using Deep Learning: An Investigation Using the DDSM Dataset and a Customized AlexNet and Support Vector Machine. IEEE Access. 2023.

Tan YN, Tinh VP, Lam PD, Nam NH, Khoa TA: A transfer learning approach to breast cancer classification in a federated learning framework. IEEe Access. 11:27462-76, 2023

Majidpour J, Rashid TA, Thinakaran R, Batumalay M, Dewi DA, Hassan BA, Dadgar H, Arabi H: NSGA-II-DL: Metaheuristic optimal feature selection with Deep Learning Framework for HER2 classification in Breast Cancer. IEEE Access. 2024.

Aziz S, Munir K, Raza A, Almutairi MS, and Nawaz S: IVNet: Transfer learning based diagnosis of breast cancer grading using histopathological images of infected cells. IEEE Access. 11:127880-94, 2023

Zeng R, Qu B, Liu W, Li J, Li H, Bing P, Duan S, Zhu L: FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification. IEEE Access. 2024.

Jadoon EK, Khan FG, Shah S, Khan A, Elaffendi M: Deep learning-based multi-modal ensemble classification approach for human breast cancer prognosis. IEEE Access. 2023.

Kausar T, Lu Y, Kausar A: Breast Cancer Diagnosis Using Lightweight Deep Convolution Neural Network Model. IEEE Access. 11:124869-86, 2023

Umer MJ, Sharif M, Kadry S, Alharbi A: Multi-class classification of breast cancer using 6b-net with deep feature fusion and selection method. Journal of Personalized Medicine. 12(5):683, 2022

Sani Z, Prasad R, Hashim EK: Breast cancer classification using equivariance transition in group convolutional neural networks. IEEE Access. 11:28454-65, 2023

Thwin SM, Malebary SJ, Abulfaraj AW, Park HS: Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks. Technologies. 12(2):16, 2024

Fatima M, Khan MA, Shaheen S, Almujally NA, and Wang SH: B2C3NetF2: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection. CAAI transactions on intelligence technology. 8(4):1374-90, 2023

Atban F, Ekinci E, Garip Z: Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomedical Signal Processing and Control. 81:104534, 2023

Sharmin S, Ahammad T, Talukder MA, Ghose P: A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access. 2023.

Hu Y, Xu C, Li Z, Lei F, Feng B, Chu L, Nie C, Wang D: Detail enhancement multi-exposure image fusion based on homomorphic filtering. Electronics. 11(8):1211, 2022

Cruz-Ramos C, García-Avila O, Almaraz-Damian JA, Ponomaryov V, Reyes-Reyes R, Sadovnychiy S: Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features. Entropy. 25(7):991, 2023

Agarwal S, Rattani A, Chowdary CR: A comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detection. Pattern Recognition Letters. 147:34-40, 2021

Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics. 12(6):610-21, 2007.

Amir S, Gandelsman Y, Bagon S, Dekel T: Deep vit features as dense visual descriptors. arXiv preprint arXiv:2112.05814. 2(3):4, 2021

Nair SS, Subaji M. A survey on applying handcrafted and learned feature extraction for breast cancer classification using machine learning and deep learning algorithms. International Journal of Computer Science and Information Security (IJCSIS). 21(7), 2023.

Naji MA, El Filali S, Aarika K, Benlahmar EH, Abdelouhahid RA, Debauche O: Machine learning algorithms for breast cancer prediction and diagnosis. Procedia Computer Science. 191:487-92, 2021

Abunasser BS, AL-Hiealy MR, Zaqout IS, Abu-Naser SS: Breast cancer detection and classification using deep learning Xception algorithm. International Journal of Advanced Computer Science and Applications. 13(7), 2022

Lilhore UK, Sharma YK, Shukla BK, Vadlamudi MN, Simaiya S, Alroobaea R, Alsafyani M, Baqasah AM. Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification. Scientific Reports. 15(1):12082, 2025.

Agrawal R, Singh NP, Shelke NA, Tripathi KN, Singh RK. CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework. Multimedia Tools and Applications 84(15):15501-26, 2025.

Aguerchi K, Jabrane Y, Habba M, Ameur M, Hassani AH. Enhancing Automated Breast Cancer Detection: A CNN-Driven Method for Multi-Modal Imaging Techniques. Journal of Personalized Medicine 15(10):467, 2025.

Vijayalakshmi S, Pandey BK, Pandey D, Lelisho ME. Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques. Scientific Reports 15(1):22212, 2025.

Comments (0)

No login
gif