Could breast cancer detection become faster, more accurate, and accessible to underserved communities? Associate Professor Dr. Deshinta Arrova Dewi from the Faculty of Data Sciences and Information Technology at INTI International University believes so. Along with her team, they are developing an artificial intelligence (AI)-powered approach that could reshape breast cancer diagnostics.
By integrating AI and machine learning into medical imaging, their research enhances diagnostic accuracy while making screening more efficient. “Our study focuses on leveraging advanced computational models to improve healthcare diagnostics,” said Dr Deshinta. The AI system uses Convolutional Neural Networks (CNN) and Transfer Learning to analyse breast ultrasound images, delivering rapid and precise assessments. This innovation could be particularly valuable in regions with limited access to medical professionals.
Associate Professor Dr. Deshinta Arrova Dewi from the Faculty of Data Sciences and Information Technology at INTI International University and her collaborators have developed a highly accurate, AI-powered approach to breast cancer diagnosis.
Traditional diagnostic methods rely on manual interpretation by radiologists and pathologists, a process that can be time-consuming and inconsistent. Dr. Deshinta’s AI-driven model automates analysis, detecting patterns that may escape human observation. It has demonstrated an impressive 90.12% accuracy rate. The research team collaborated with medical professionals who tested the system and found it effective, particularly in time-sensitive cases and large-scale screenings. “Doctors appreciated the model’s ability to provide accurate and consistent results,” Dr. Deshinta shared. However, they also emphasised that AI should support clinical expertise rather than replace it.
Their research redefines how breast cancer is detected and managed by harnessing AI and machine learning, with far-reaching implications for global healthcare.
Beyond improving diagnostics, AI could advance personalised medicine by tailoring treatment plans to individual patients. “AI systems can identify patterns in data that go beyond human capability, leading to earlier and more precise diagnoses,” she explained. The technology also has the potential to bridge healthcare disparities by providing cost-effective diagnostic solutions in underserved areas, making quality healthcare more accessible. However, AI in medicine presents challenges, particularly in data security and algorithmic fairness.
“Protecting patient data and ensuring ethical AI use is crucial,” Dr. Deshinta noted. Her team has implemented strict protocols to safeguard information and mitigate biases, ensuring the model performs equitably across diverse populations.
This research results from an international collaboration with contributors from the University of Mauritius, Spoon Consulting Mauritius, Universitas Bina Darma in Indonesia, and Shinawatra University in Thailand. Together, they have developed a desktop application that translates their findings into a practical tool for healthcare professionals. Dr. Deshinta’s team plans to expand the model’s capabilities by incorporating additional imaging modalities such as mammography and MRI. “A multi-modal approach could further enhance diagnostic accuracy, especially in complex cases,” she said.
With AI-driven solutions like this, breast cancer care is entering a new era where technology supports medical professionals and makes life-saving diagnostics more widely accessible
Associate Professor Dr. Deshinta Arrova Dewi and her research collaborators—Dr. Sheeba Armoogum from the University of Mauritius, Kezhilen Motean from Spoon Consulting Mauritius, and Dr. Tri Basuki Kurniawan from Universitas Bina Darma, Indonesia—held an online discussion regarding their project.