The Integration of Deep Learning Techniques and Big Data Analytics for Improved Breast Cancer Diagnosis and Treatment: A Systematic Review
Hambisa Mitiku Gebre, Getachew Mamo Wegari · 2024
Background:- Breast cancer is one of the most common and lethal diseases in the world. Traditional breast cancer diagnostic and prognosis procedures usually involve significant human talent and can be time-consuming and subjective, leading to potential errors and treatment delays. Two recent technical advancements, deep learning, and big data analytics are promising to improve breast cancer diagnosis and therapy. As a result, this systematic review aims to examine specific papers that describe deep learning techniques and big data analytics in the context of breast cancer diagnosis and prediction.Methods: Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), peer-reviewed articles published in the English language from January 2020 to August 2023 were selected from electronic databases such as PubMed, ACM, Digital Library, and Science Direct, as well as citations and manual searches. This review paper takes into account papers about deep learning algorithms, big data analytics, the efficacy of deep learning and big data analytics, as well as the problems and limitations of merging deep learning methods with big data analytics. Articles that were not original or in English were not included.Result: - Ten articles were identified for this review. The finding showed that deep-learning techniques play a great role in analyzing vast datasets to identify malignant cells or tumors, aiding radiologists in accurate diagnoses and improving patient outcomes. Big data analytics in breast cancer diagnosis and treatment can improve accuracy, efficiency, and patient-centered care. Deep learning techniques are utilized with big data, enhancing screening test accuracy and guiding diagnostic procedures, especially for image-based scanning that leads to early breast cancer identification, improving patient outcomes, and potentially enhancing diagnostics. Obtaining huge volumes of high-quality data for training deep learning models is a challenge and limitation of integrating deep learning algorithms with big data analytics; due to data privacy, data fragmentation across multiple healthcare systems, and restricted access to annotated datasets.Conclusion: Deep learning techniques combined with big data analytics have a significant potential for improving breast cancer detection and therapy.