Deep Learning Approach for Early Prediction and Diagnosis of Breast Cancer Tumors

To interpret the MRI data, the study uses Convolutional Neural Networks (CNN), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), and Naive Bayes (NB). In this article, the use of machine learning models for breast cancer diagnosis is carefully examined. The experiment's findings revealed numerous interesting findings and conclusions. The CNN model fared the best and had the most promise with an accuracy score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6. 4 \%}$</tex>. This finding reveals the accuracy with which deep learning systems can identify breast cancer in imaging data. This astonishing level of accuracy was made possible by the CNN's expertise in feature extraction and image identification. KNN also performed well, demonstrating its applicability for context-aware breast cancer detection with an accuracy of 93.7 %. It has been demonstrated that the KNN's proximity-based classification algorithm can accurately classify related events with high recall and precision rates. Despite being slightly less accurate than CNN and KNN, SVM and NB fared brilliantly, with accuracy rates of 92.2 % and 90.33 percent, respectively. These models demonstrated how machine learning may provide flexible and effective diagnostic skills for the diagnosis of breast cancer.