Advancements in Breast Cancer Detection Using Machine Learning Techniques for Early and Accurate Prediction
Sumit Kumar, V. Kathiresan, R. M. Gomathi, R. Karthik, S. Sugantha Priya, C. Naveena Jasmine · 2024
Breast cancer continues to be a significant worldwide health concern, which emphasises the importance of an accurate and prompt diagnosis. In this study, CT and MRI scan images and associated medical data will be used to assess the potential of machine learning algorithms for breast cancer prediction. Convolutional neural networks (CNN), k-nearest neighbours (KNN), recurrent neural networks (RNN), support vector machines (SVM), and random forest (RF) were among the machine learning models we used in our research. We evaluated their performance in terms of accuracy, precision, recall, and F1-Score. The CNN test results showed an incredible 98.7% accuracy, proving the efficacy of their picture analysis. While each model offers unique benefits, the overall findings highlight the importance of combining several strategies to improve breast cancer diagnosis. The accurate evaluation of model performance provided by the accuracy, recall, and F1-Score measurements made it easier to comprehend the models' efficacy. These findings may profoundly change the early detection of breast cancer, improving patient outcomes and promoting scholarly research.