AI and Neural Networks Help Speed Up Breast Cancer Diagnosis One of the most stressful parts of a cancer diagnosis is the waiting, waiting for test results, waiting to understand the next steps, and waiting to begin treatment. A new study published in Scientific Reports explores how artificial intelligence (AI) and deep-learning (DL) models can reduce the time it takes for medical professionals to classify breast cancer lesions from ultrasound images . Faster and More Accurate Diagnosis with AI Breast ultrasound imaging is widely used to detect and differentiate between benign (non-cancerous) and malignant (cancerous) tumors. However, interpreting these images can be complex and time-consuming for doctors. AI and neural networks offer a faster, more precise way to analyze these images, making early diagnosis and treatment more effective. Dr. Keka Biswas , a lecturer in biological sciences at the University of Alabama in Huntsville (UAH), worked with a team of experts, including biologists, mathematicians, and statisticians, to explore AI’s role in medical imaging. According to Dr. Biswas, deep learning mimics how the human brain processes large amounts of complex data, making it ideal for medical image analysis . “AI is already helping in various fields like text classification and medical imaging,” she explains. “With AI, we can speed up the process during surgery and better understand cancer staging in real-time.” A Personal Connection to the Research Dr. Biswas’ research became even more personal when she was diagnosed with cancer herself in 2024. She recalls how difficult it was to wait for test results. Her experience reinforced the need for faster and more precise diagnostic tools, motivating her to push forward with this research. “ It took weeks to find out what stage my cancer was in, ” she says. “ My oncologist was frustrated because even with all the imaging, we still didn’t have clear answers right away .” How AI Analyzes Breast Cancer Imaging Deep-learning models can analyze both the internal and external features of tumors, including their shape, borders, and how they affect surrounding tissues. These factors help doctors determine the severity of the cancer and how aggressively it is spreading. The study focused on how AI can: [object Object], [object Object], [object Object] Researchers used neural network datasets, collections of labeled medical images, to train AI models. The study tested three deep-learning models: VGG16 , MobileNetV2 , and EfficientNetB7 , using a method called transfer learning to improve accuracy. AI in medical imaging has the potential to revolutionize cancer diagnosis by making it quicker, more accurate, and accessible . While more research and clinical trials are needed, this technology is already showing promising results. By combining expertise in AI, biology, and medical imaging, researchers like Dr. Biswas hope to improve patient outcomes and reduce the anxiety caused by long wait times for test results.
