Breast Cancer Detection and Classification with Data-Augmented Ensemble Model

Medical image classification plays a crucial role in modern healthcare, aiding in the early detection and diagnosis of various diseases. This paper introduces an advanced machine-learning model for the classification of medical images, specifically focusing on its application in the context of cancer detection. The proposed model, named MLM-EO (Multi-Level Morphological Extraction Optimization), integrates cutting-edge optimization techniques with machine learning algorithms to achieve highly accurate classification results. The study encompasses several key phases, including image segmentation, feature extraction, and classification. In the segmentation phase, MLM-EO demonstrates exceptional performance in accurately delineating target objects within medical images. The feature extraction process extracts meaningful information from the images, enhancing the model's ability to capture relevant patterns and characteristics. The classification results showcase the model's proficiency in categorizing medical images into “Cancer” and “Non-Cancer” classes, with associated probability estimates. Comparative analysis with other classification methods reveals MLM-EO's superiority in terms of accuracy, precision, recall, and F1-Score. This research contributes to the ongoing efforts to advance medical image analysis and improve disease diagnosis. The MLM-EO model exhibits great promise as a valuable tool for healthcare professionals, offering the potential to enhance the accuracy and efficiency of cancer detection. Further validation and deployment in clinical settings are essential to fully unlock its potential and impact on patient care.