The Smart Performance Comparison of AI-based Breast Cancer Detection Models
Sana Samreen, Abdul Sajid Mohammed, Anuteja Reddy Neravetla, Nasmin Jiwani, J. Logeshwaran
Abstract
The smart performance comparison of AI-based breast cancer detection models is an important research topic in the healthcare industry. It is used to compare and evaluate different AI-based models that are used to diagnose breast cancer. These models are mainly developed using machine learning, computer vision, or deep learning techniques. The methods used to compare and evaluate these models can vary depending on the purpose of the comparison. This can include comparing accuracy, precision, recall, or f-measure of different models. Furthermore, other criteria such as stability, reliability, explain ability, speed, and cost-effectiveness may be taken into consideration when evaluating the models. These models have achieved high sensitivity and specificity rates, outperforming traditional detection methods. However, the performance of the AI models varies based on the type of imaging technique and dataset used. Further, the research also highlights the need for more diverse and inclusive datasets to avoid potential biases in the AI models. The results from this comparison provide valuable insight into the performance of AI-based breast cancer detection models and can help healthcare professionals and researchers select and deploy the best model for their particular applications.