A Novel Approaches of Detecting Breast Cancer with Hybrid Models: Techniques and Challenges

Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Early detection plays a critical role in improving patient outcomes and survival rates. In recent years, the integration of hybrid models in breast cancer detection has shown promising results by combining the strengths of different machine learning algorithms and techniques. This abstract presents a comprehensive review of the application of hybrid models for breast cancer detection, highlighting their potential advantages and challenges. The proposed hybrid models combine various techniques, such as feature selection, data preprocessing, and classification algorithms, to enhance the accuracy and efficiency of breast cancer detection systems. Feature selection methods, including genetic algorithms, particle swarm optimization, and principal component analysis, are utilized to identify the most informative features from mammographic images or clinical data. These selected features are then fed into classification algorithms, such as support vector machines, random forests, artificial neural networks, or deep learning models, for accurate diagnosis. Furthermore, the hybrid models often incorporate data preprocessing techniques to improve the quality and consistency of the input data. Preprocessing steps may involve image enhancement, normalization, noise removal, or data augmentation, depending on the specific requirements of the model. By optimizing the feature extraction process and improving data quality, hybrid models aim to achieve higher sensitivity, specificity, and overall performance in breast cancer detection. Several studies have reported the successful implementation of hybrid models in breast cancer detection. These models have demonstrated superior performance compared to individual algorithms or traditional approaches. The combination of different algorithms allows for better capturing of complex patterns and subtle variations in breast images or clinical data, leadin