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

Abstract

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, leading to more accurate and reliable detection outcomes. Despite the promising results, there are challenges associated with the development and application of hybrid models for breast cancer detection. The selection andfine-tuning of the various components in the hybrid models require careful consideration, as the performance heavily depends on the integration of different algorithms and techniques. Moreover, the interpretability and explainability of hybrid models need to be addressed to gain trust from healthcare professionals and patients. The integration of hybrid models in breast cancer detection has shown great potential in improving accuracy and efficiency. These models leverage the strengths of multiple algorithms and techniques, allowing for enhanced feature selection, data preprocessing, and classification. While challenges remain, further research and development in this area hold promise for advancing breast cancer detection systems, ultimately leading to earlier diagnoses and improved patient outcomes. International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 07 | July - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM24204 | Page 2 Introduction: Breast cancer is a significant health concern affecting millions of women worldwide. Early detection and accurate diagnosis play a crucial role in improving treatment outcomes and patient survival rates. Deep learning, a subfield of machine learning, has emerged as a powerful approach for breast cancer detection, leveraging its ability to automatically learn intricate patterns and features from complex data. In this article, we delve into the application of deep learning models for breast cancer detection, exploring various architectures,datasets, and challenges associated with their implementation.