Email this article Vision beyond the human eye: advancing early breast cancer detection through computer vision and ai-enhanced imaging
Abstract
This paper reviews the application of computer vision and artificial intelligence (AI) in enhancing breast cancer detection, exploring how deep learning models, particularly Convolutional Neural Networks (CNNs), augment traditional screening techniques. The review examines the current state of computer vision applications in breast cancer detection, emphasizing deep learning-based approaches, and discusses how CNNs are integrated into clinical workflows, the empirical evidence supporting their effectiveness, and the practical challenges involved in their clinical adoption. The methodology also includes a deep learning-based approach to classify and segment breast ultrasound images using a publicly available dataset. CNN-based systems demonstrate performance on par with or even surpassing human radiologists in specific diagnostic tasks. Studies show that MobileNetV3, a lightweight CNN, holds strong potential for integration into edge AI systems for point-of-care diagnostics, as well as in privacy-preserving frameworks such as federated learning. The MobileNetV3-based classification model demonstrated robust performance across the three diagnostic categories: normal, benign, and malignant, with an overall test set accuracy of 91.2%. Key performance metrics, including precision (benign: 0.85, malignant: 0.74, normal: 0.83), recall (benign: 0.84, malignant: 0.74, normal: 0.88), F1-score (benign: 0.85, malignant: 0.74, normal: 0.86), and accuracy (0.82), are examined to evaluate the efficacy of these AI-driven approaches. The review identifies emerging trends, such as multi-modal learning and federated learning, which aim to enhance model robustness and privacy. The integration of AI into clinical workflows holds promise for improving diagnostic accuracy and reducing healthcare disparities by expanding access to high-quality screening services. This paper contributes to a deeper understanding of how AI-driven innovations are reshaping breast cancer detection and inspires further research toward their responsible and widespread implementation.
Received on, 11 May 2025
Accepted on, 18 June 2025
Published on, 02 October 2025
Full Text:
PDFReferences
N. Baughan, L. Douglas, and M. L. Giger, “Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening,” J Breast Imaging, vol. 4, no. 5, pp. 451–459, Oct. 2022, doi: 10.1093/jbi/wbac052.
M. Masud et al., “Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images,” ACM Trans Internet Technol, vol. 21, no. 4, pp. 1–17, Nov. 2021, doi: 10.1145/3418355.
A. Ghoneim, G. Muhammad, and M. S. Hossain, “Cervical cancer classification using convolutional neural networks and extreme learning machines,” Future Generation Computer Systems, vol. 102, pp. 643–649, Jan. 2020, doi: 10.1016/j.future.2019.09.015.
M. T. R et al., “Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, p. 14, Jan. 2024, doi: 10.1007/s44196-023-00397-1.
O. Samuel Abiodun, O. P. Ejenarhome, and G. Oise, “AI-BASED MEDICAL IMAGE ANALYSIS FOR EARLY DETECTION OF NEUROLOGICAL DISORDERS USING DEEP LEARNING,” FUDMA JOURNAL OF SCIENCES, vol. 9, no. 6, pp. 322–328, Jun. 2025, doi: 10.33003/fjs-2025-0906-3697.
J. Zheng et al., “3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection,” IEEE J Biomed Health Inform, vol. 25, no. 3, pp. 764–773, Mar. 2021, doi: 10.1109/JBHI.2020.3003316.
B. E. Akilo, S. A. Oyedotun, G. P. Oise, O. C. Nwabuokei, and N. B. Unuigbokhai, “Intelligent Traffic Management System Using Ant Colony and Deep Learning Algorithms for Real-Time Traffic Flow Optimization,” Journal of Science Research and Reviews, vol. 1, no. 2, pp. 63–71, Dec. 2024, doi: 10.70882/josrar.2024.v1i2.52.
M. Robin, J. John, and A. Ravikumar, “Breast Tumor Segmentation using U-NET,” in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, Apr. 2021, pp. 1164–1167. doi: 10.1109/ICCMC51019.2021.9418447.
V. S. S, H. M S, I. H, A. K. S, and S. S, “Improved Breast Cancer Detection using Wavelet Based Image Enhancement,” in 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/ICAECA56562.2023.10200940.
G. P. Oise, S. A. Oyedotun, O. C. Nwabuokei, A. E. Babalola, and N. B. Unuigbokhai, “ENHANCED PREDICTION OF CORONARY ARTERY DISEASE USING LOGISTIC REGRESSION,” FUDMA JOURNAL OF SCIENCES, vol. 9, no. 3, pp. 201–208, Mar. 2025, doi: 10.33003/fjs-2025-0903-3263.
G. P. Oise et al., “DECENTRALIZED DEEP LEARNING IN HEALTHCARE: ADDRESSING DATA PRIVACY WITH FEDERATED LEARNING,” FUDMA JOURNAL OF SCIENCES, vol. 9, no. 6, pp. 19–26, Jun. 2025, doi: 10.33003/fjs-2025-0906-3714.
G. P. Oise et al., “YOLOv8-DeepSORT: A High-Performance Framework for Real-Time Multi-Object Tracking with Attention and Adaptive Optimization,” Journal of Science Research and Reviews, vol. 2, no. 2, pp. 92–100, May 2025, doi: 10.70882/josrar.2025.v2i2.50.
G. P. Oise, S. A. Oyedotun, O. C. Nwabuokei, A. E. Babalola, and N. B. Unuigbokhai, “ENHANCED PREDICTION OF CORONARY ARTERY DISEASE USING LOGISTIC REGRESSION,” FUDMA JOURNAL OF SCIENCES, vol. 9, no. 3, pp. 201–208, Mar. 2025, doi: 10.33003/fjs-2025-0903-3263.
M. F. Aslan, K. Sabanci, and E. Ropelewska, “A CNN-Based Solution for Breast Cancer Detection With Blood Analysis Data: Numeric to Image,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), IEEE, Jun. 2021, pp. 1–4. doi: 10.1109/SIU53274.2021.9477801.
G. P. Oise and S. Konyeha, “Deep Learning System for E-Waste Management,” in The 3rd International Electronic Conference on Processes, Basel Switzerland: MDPI, Oct. 2024, p. 66. doi: 10.3390/engproc2024067066.
G. P. Oise and O. J. Akpowehbve, “Systematic Literature Review on Machine Learning Deep Learning and IOT-based Model for E-Waste Management,” International Transactions on Electrical Engineering and Computer Science, vol. 3, no. 3, pp. 154–162, Sep. 2024, doi: 10.62760/iteecs.3.3.2024.94.
M. Lu, X. Xiao, Y. Pang, G. Liu, and H. Lu, “Detection and Localization of Breast Cancer Using UWB Microwave Technology and CNN-LSTM Framework,” IEEE Trans Microw Theory Tech, vol. 70, no. 11, pp. 5085–5094, Nov. 2022, doi: 10.1109/TMTT.2022.3209679.
H. Soltani, M. Amroune, I. Bendib, and M. Y. Haouam, “Breast Cancer Lesion Detection and Segmentation Based On Mask R-CNN,” in 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI), IEEE, Sep. 2021, pp. 1–6. doi: 10.1109/ICRAMI52622.2021.9585913.
S. Ruban, M. Moosa Jabeer, and R. Shenoy Basti, “Improvising Breast Cancer detection using CNN, VGG and SSD Algorithms,” in 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), IEEE, Aug. 2022, pp. 1772–1776. doi: 10.1109/ICICICT54557.2022.9918009.
N. B. Nair, T. Singh, A. Thakur, and P. Duraisamy, “Deployment of Breast Cancer Hybrid Net using Deep Learning,” in 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Oct. 2022, pp. 1–6. doi: 10.1109/ICCCNT54827.2022.9984513.
Z. Aizaz, K. Khare, A. Khursheed, and A. Tirmizi, “Pix2Pix Generative adversarial Networks (GAN) for breast cancer detection,” in 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), IEEE, Nov. 2022, pp. 1–5. doi: 10.1109/IMPACT55510.2022.10029087.
P. K. Samanta, N. K. Rout, and G. Panda, “A novel deep CNN model for improved breast cancer detection using ultrasound images,” in 2023 International Conference on Communication, Circuits, and Systems (IC3S), IEEE, May 2023, pp. 1–4. doi: 10.1109/IC3S57698.2023.10169383.
S. Bhise, S. Gadekar, A. S. Gaur, S. Bepari, D. Kale, and S. Aswale, “Detection of Breast Cancer Using Machine Learning and Deep Learning Methods,” in 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), IEEE, Apr. 2022, pp. 1–6. doi: 10.1109/ICIEM54221.2022.9853080.
G. Oise and S. Konyeha, “E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH,” FUDMA JOURNAL OF SCIENCES, vol. 8, no. 3, pp. 17–24, Jul. 2024, doi: 10.33003/fjs-2024-0804-2579.
H. S. Das, A. Das, A. Neog, S. Mallik, K. Bora, and Z. Zhao, “Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach,” Front Genet, vol. 13, Jan. 2023, doi: 10.3389/fgene.2022.1097207.
G. P. Oise, S. A. Oyedotun, O. C. Nwabuokei, A. E. Babalola, and N. B. Unuigbokhai, “ENHANCED PREDICTION OF CORONARY ARTERY DISEASE USING LOGISTIC REGRESSION,” FUDMA JOURNAL OF SCIENCES, vol. 9, no. 3, pp. 201–208, Mar. 2025, doi: 10.33003/fjs-2025-0903-3263.
I.-R. Macaşoi and V.-E. Neagoe, “Breast Cancer Detection using Thermal Infrared Image Analysis based on Dempster-Shafer Decision Fusion of CNN Classifiers,” in 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), IEEE, Jun. 2023, pp. 01–06. doi: 10.1109/ECAI58194.2023.10194213.
S. Rani, M. Memoria, M. Rani, and R. Kumar, “Breast Cancer Detection Using Mammographic Images over Convolutional Neural Network,” in 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, Jan. 2023, pp. 73–78. doi: 10.1109/Confluence56041.2023.10048862.
G. P. Oise, O. C. Nwabuokei, O. J. Akpowehbve, B. A. Eyitemi, and N. B. Unuigbokhai, “TOWARDS SMARTER CYBER DEFENSE: LEVERAGING DEEP LEARNING FOR THREAT IDENTIFICATION AND PREVENTION,” FUDMA JOURNAL OF SCIENCES, vol. 9, no. 3, pp. 122–128, Mar. 2025, doi: 10.33003/fjs-2025-0903-3264.
Y. V, M. M, A. R R, A. K. S, and S. S, “Towards Improving Breast cancer detection in Ultrasound Images using Deep Transfer Learning,” in 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/ICAECA56562.2023.10200302.
O. A. Muyiwa and B. H. Adejumo, “Leveraging deep learning technology for enhancing printing press quality,” Advances in Computing and Engineering, vol. 4, no. 2, p. 109, Oct. 2024, doi: 10.21622/ACE.2024.04.2.951.
P. Praveena and N. S. Kumar, “Breast Cancer Detection Through Semantic Segmentation of MRI Images with DMFTNet,” SN Comput Sci, vol. 4, no. 5, p. 448, Jun. 2023, doi: 10.1007/s42979-023-01879-x.
H. Mahmoud, “Modern architectures convolutional neural networks in human activity recognition,” Advances in Computing and Engineering, vol. 2, no. 1, p. 1, Jun. 2022, doi: 10.21622/ace.2022.02.1.001.
B. Abunasser, M. R. AL-Hiealy, I. Zaqout, and S. Abu-Naser, “Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning,” Asian Pacific Journal of Cancer Prevention, vol. 24, no. 2, pp. 531–544, Feb. 2023, doi: 10.31557/APJCP.2023.24.2.531.
A. A. Goudah, M. Jarofka, M. El-Habrouk, D. Schramm, and Y. G. Dessouky, “Object detection in inland vessels using combined trained and pretrained models of YOLO8,” Advances in Computing and Engineering, vol. 3, no. 2, p. 64, Nov. 2023, doi: 10.21622/ACE.2023.03.2.064.
R. K. Yadav, P. Singh, and P. Kashtriya, “Diagnosis of Breast Cancer using Machine Learning Techniques -A Survey,” Procedia Comput Sci, vol. 218, pp. 1434–1443, 2023, doi: 10.1016/j.procs.2023.01.122.
G. Oise and S. Konyeha, “Environmental impacts in e-waste management using deep learning,” Discover Artificial Intelligence, vol. 5, no. 1, p. 210, Aug. 2025, doi: 10.1007/s44163-025-00376-9.
S. Ahmad, S. Ur Rehman, A. Iqbal, R. K. Farooq, A. Shahid, and M. I. Ullah, “Breast Cancer Research in Pakistan: A Bibliometric Analysis,” Sage Open, vol. 11, no. 3, Jul. 2021, doi: 10.1177/21582440211046934.
A. U. Haq et al., “Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques,” IEEE Access, vol. 9, pp. 22090–22105, 2021, doi: 10.1109/ACCESS.2021.3055806.
H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro, and A. Liotta, “Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning,” Comput Methods Programs Biomed, vol. 223, p. 106951, Aug. 2022, doi: 10.1016/j.cmpb.2022.106951.
DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1343
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Godfrey Perfectson Oise, Babalola Eyitemi Akilo, Joy Akpowehbve Odimayomi, Unuigbokhai Nkem Belinda, Chioma Julia Onwuzo, Onoriode Michaele Atake, Sofiat Kehinde Bakare
Advances in Computing and Engineering
E-ISSN: 2735-5985
P-ISSN: 2735-5977
Published by:
Academy Publishing Center (APC)
Arab Academy for Science, Technology and Maritime Transport (AASTMT)
Alexandria, Egypt
