Introducing CWDCMFE-MBRC Technique for Breast Cancer Detection: A Comparative Survey of Novel Approaches

Breast cancer poses a significant public health concern for countless women worldwide. Detecting breast cancer in its early stages plays a pivotal role in enhancing patient outcomes and reducing mortality rates. Various imaging techniques have been developed to aid in the early detection of breast cancer., including mammography, ultrasound, MRI, thermography, and x-ray. However, these techniques have their limitations with regard to accuracy, sensitivity, specificity, and precision. In recent years, ways of deep learning have emerged as a promising approach for breast cancer detection. Convolutional neural networks (CNNs) have demonstrated high accuracy in detecting breast cancer from medical images. However, CNNs have certain limitations, one of which is the need for substantial amounts of labeled data. This requirement can present challenges, particularly in the field of medical imaging, where acquiring a large volume of labeled data may be difficult. Moreover, CNNs suffer from a lack of interpretability, posing challenges in comprehending the decision-making process behind the network's predictions. This absence of interpretability makes it arduous to gain insights into how the network arrives at its conclusions. To address these limitations, researchers have developed alternative deep learning methods for breast cancer detection. One such method is the Complex Wavelet Deep Convolutional Multiple Feature Extraction-MBRC (CWDCMFE-MBRC) method. This method brings together the benefits of wavelet transform, deep convolutional neural networks, and multiple feature extraction to achieve high accuracy in breast cancer detection. The CWDCMFE-MBRC method involves several steps. First, the input picture is broken down into sub-bands using the complex wavelet transform. Then, the sub-bands are passed through a deep convolutional neural network to extract high-level features. Next, multiple features are extracted from the output of the neural network using PCA and LDA. Finally, the resulting characteristics are used as input to the MBRC classifier to detect breast cancer. The CWDCMFE-MBRC method has several advantages over CNNs and other traditional imaging techniques. Firstly, it achieves high accuracy in breast cancer detection while requiring less labeled data than CNNs. Additionally, the method provides interpretability through the use of multiple feature extraction techniques, making it easier to understand how the network arrives at its predictions. Deep learning methods, such as CNNs and the CWDCMFE-MBRC method, have shown promise in improving breast cancer detection accuracy. This study aims to conduct a comprehensive survey of breast cancer detection methods, including both traditional imaging techniques and deep learning methods, provide a complete snapshot of the current state of the field.