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The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes. [ABSTRACT FROM AUTHOR]

In recent years, breast cancer is observed to be more prevalent among women. Timely and accurate prediction of the disease enables health care professionals to adopt a strong decision-making system in treatment optimization. Thus, this study employed a deep learning-based strategy for efficient breast cancer diagnosis. In this study, the feature extraction is efficiently performed with the Deep CNN and VGG-16 and the classification process has been processed that intends to classify appropriate features by using the newly introduced CBGAT (CNN-Bi-LSTM-GRU-AM) technique that helps to enhance the accuracy of image recognition and to predict the breast cancer without any human intervention. The complete approach uses MIAS and the CBIS-DDSM dataset for the process of training and for the evaluations. The proposed study aims in minimising the cases of human intervention of breast cancer diagnosis. The approach DL architectures comprising both the Deep CNN and VCG-16, for the process of feature extraction from the breast cancer diagnosis. Further, the proposed approach is embedded with the PCA in enhancing the procedures of feature extraction. This PCA is used in the proposed study as a fusion technique. PCA is used as one of a dimensionality reduction technique, used in capturing the more of exact information from the features. The combination of CNN-Bi-LSTM-GRU-AM classification approach used in the study is considered in analysing the sequential information and in capturing the long-term dependencies also used in focusing the salient features. The Deep-CNN and VCG-16 are incorporated for leveraging the strengths of each of the network, and are used in capturing the range of features. This integration aids in enhancing the representation and the discriminative power of the extracted features. Influencing the use of AM in the classification process is one more an added advantage of the proposed approach. This AM allows the model to be focused on the vita regions of features within the images provided as input.The experimental implementation and performance analysis of the proposed system is undertaken. The accuracy of each proposed classification technique is discussed with respect to their dataset. The analytical outcomes explore that the proposed model is more effective than the traditional methods as the proposed model highlights a higher accuracy rate, F1 score, sensitivity, specificity, and Area under the curve (AUC). The proposed also determined the importance of breast cancer prediction. [ABSTRACT FROM AUTHOR]