Mitigating Missed Diagnoses: A Patch-Based Deep Learning Framework for Breast Cancer Classification

Breast cancer remains a significant public health concern, with early and accurate diagnosis being crucial for effective treatment. Machine learning (ML) algorithms offer promising support in breast cancer classification. This study evaluates the performance of various ML techniques (GNB & LR 97%, SVM 96%, DT 95%), using well known a Kaggle dataset with 699 instances and 10 attributes. The findings highlight the potential of these algorithms to enhance diagnostic accuracy, aiding in early detection and treatment. Keywords: Breast cancer, Machine learning, Classification, Decision trees, Logistic regression, Naive Bayes, Support vector machines.