Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes.

Early detection of breast cancer (BC) is essential for effective treatment and improved prognosis. This study compares the performance of various machine learning (ML) algorithms, including convolutional neural networks (CNNs), logistic regression (LR), support vector machines (SVMs), and Gaussian naive Bayes (GNB), on two key datasets, Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Histopathological Image Classification (BreaKHis). For the BreaKHis dataset, the CNN achieved an impressive accuracy of 92%, with precision, recall, and F1 score values of 91%, 93%, and 91%, respectively. In contrast, LR achieved 88% accuracy, with corresponding precision, recall, and F1 score values of 86%, 87%, and 89%, respectively. SVM and GNB demonstrated 90% and 84% accuracy, respectively, with similar precision, recall, and F1-score metric performances. In the WDBC dataset, LR achieved the highest accuracy of 97.5%, with nearly 97% values for precision, recall, and F1 score. In contrast, CNN attained 96% accuracy with equal recall, precision, and F1 score values of 96%. SVM and GNB followed closely with 95% and 94% accuracy, respectively. Minimising the false negative rate (FNR) and false omission rate (FOR) is vital for improving model reliability, with the LR excelling in the WDBC dataset (FNR: 5.9%, FOR: 4.8%) and the CNN performing best in the BreaKHis dataset (FNR: 8.3%, FOR: 7.0%). The results demonstrate that CNN outperforms traditional models across both datasets, highlighting its potential for early and accurate BC detection.