Cutting-Edge Neural Networks Elevating Breast Cancer Diagnosis Accuracy through Image Analysis
S Kanagamalliga, Dixit Varma · 2024
The diagnosis of breast cancer is being enhanced by cutting-edge neural networks, with lightweight Convolutional Neural Networks (CNNs) optimized for mobile devices playing a pivotal role. Early-stage prediction is enabled through the automatic analysis of breast imaging data, such as mammograms and ultrasounds, directly on portable devices. Models like MobileNet, designed for efficient image analysis with minimal computational power, are ideally suited for smartphones and tablets. By incorporating cloud-based transfer learning, pretrained networks can be used to improve diagnostic accuracy, even in areas with limited access to advanced medical facilities. Real-time image analysis is facilitated, allowing for faster and more precise breast cancer screening at the point of care. Through mobile applications, patients are able to conduct preliminary self-screening, reducing dependence on centralized medical systems and promoting early recognition, which is critical for successful treatment. This method has the potential to significantly increase access to breast cancer screening, especially in underserved regions, improving early diagnosis and patient outcomes. By combining CNN technology with mobile devices, a more accessible, scalable, and efficient approach to breast cancer diagnosis is provided, offering advanced medical tools directly to patients.