A Deep Learning-Based Model for Early Self-Detection of Breast
Cancer

SAIDU; Hayatu Alhaji; Dr. Yusuf Musa Malgwi; Mohammed; Usman

doi:10.56201/ijcsmt.v10.no3.2024.pg1.18

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A Deep Learning-Based Model for Early Self-Detection of Breast Cancer

SAIDU, Hayatu Alhaji, Dr. Yusuf Musa Malgwi and Mohammed, Usman

Abstract

The advent of machine learning and artificial intelligence has revolutionized the field of medical diagnostics, particularly in the early detection of breast cancer. This study presents the development and evaluation of a high-accuracy breast cancer detection model using the Wisconsin Breast Cancer dataset (WBCD). The model's performance was meticulously analyzed, focusing on its ability to accurately distinguish between benign and malignant cases. The results demonstrated exceptional precision, recall, and F1 scores of 0.988 for both classes, indicating a balanced performance with minimal false positives and false negatives. The confusion matrix further highlighted the model's robustness, with a true positive and true negative rate of 494 and a minimal misclassification rate. The training progress, as depicted by the epoch plot, showed a steady increase in accuracy from 70% to nearly 99%, indicating effective learning and generalization capabilities. This study's findings underscore the potential of the developed model for early detection and timely intervention in breast cancer, offering a promising tool for clinical practice. The implications of these results are significant, suggesting a future where AI-driven diagnostics can substantially improve patient outcomes and reduce the burden of breast cancer.

Keywords

Breast Cancer DetectionMachine LearningArtificial IntelligenceWisconsin

References

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A Deep Learning-Based Model for Early Self-Detection of Breast Cancer

Abstract

Keywords

References

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