Breast Cancer Classification and Segmentation Using Machine Learning Classifiers and Convolutional Neural Networks – IJCT Volume 12 – Issue 5 | IJCTV12I5P50

One of the top causes of death in women across the globe is breast cancer and early diagnosis is crucial in enhancing survival. This paper introduces a computer-based diagnostic tool, which uses machine learning classifiers and convolutional neural nets to effectively classify and segment breast cancer. The Proposed system uses preprocessing methods that optimize mammogram images and then detects suspicious areas, feature extraction and classification by algorithms such as Support Vector Machine, Decision Tree, Random Forest, and XGBoost. Ensemble methods such as bagging, boosting and stacking are utilized to improve accuracy and minimize misclassification. A multimodal architecture is created, in which the base classifiers predictions are pooled and improved by artificial neural networks to come up with more reliable results. Besides, the convolutional neural networks are implemented to enhance the feature representation and segmentation performance, thus, more effectively distinguishing between benign and malignant tissues. The system was tested on benchmark datasets, and it can be classified with high accuracy, with the highest results of 96.5% in the case of the Random Forest and 99.3% in the case of XGBoost, and the CNN models increased the reliability of segmentation. The benefit of this hybrid setup is that it enables the interpretability of machine learning classifiers with the strong representation learning of deep networks which decreases false positives and false negatives. The paper identifies the possibility of using intelligent multimodal systems to aid radiologists in early detection and diagnosis of breast cancer, which will eventually help improve patient outcome. Such a framework offers an effective and systematic solution that can be scaled to real-time clinical uses

This propsed work presents a multimodal diagnostic system that combines classical classifiers, ensemble learning techniques, and deep neural networks to detect and segment breast cancer. The method proved to be more accurate and robust, in addition to being interpretable, than individual methods, with CNNs allowing accurate localization of lesions and stacked classifiers to improve predictive accuracy. The system integrates classification and segmentation into a single pipeline to deliver clinically relevant results that may be used to aid early and accurate diagnosis. Future research will emphasize testing the framework on larger and more heterogeneous datasets and other imaging modalities to further justify its usefulness in practice in real-life clinical settings.

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