<p>Breast cancer is prevalent among the female population. Early detection has been shown to greatly improve the chances of long-term survival for patients with breast cancer. Nevertheless, the early detection of early-stage breast cancer is difficult due to noise, irregular tissue patterns, and the inability to correctly classify the tumor. This makes the current breast cancer detection systems less reliable and less accurate. In this paper, a reliable system is proposed to improve the early detection and classification of breast cancer by ensuring that images are clear and classified correctly. The proposed system uses a three-step approach: data acquisition, preprocessing, and Detection and classification. First, data is acquired from a Breast Cancer dataset. The images are then preprocessed using Isolated Kalman Filtering (IKF), which improves Image resizing, data cleaning, and normalizations. Next, the Mixture Clustering-Based Attention Neural Network (MCANN) will be employed to detect tumor regions and classify them as benign and malignant. The proposed method is able to correctly detect tumor regions, assign dynamic weights to critical variations in tissues, and effectively handle irregular tissue patterns. The experimental result clearly reveals that the proposed MIST-EBCD-MCANN method is more effective than the existing methods, namely EBCD-CNN, BCD-CMI-YOLOv5, and TDCP-MBI-DNN, with an accuracy of 96% and 97%, precision of 98% and 97%, recall of 97% and 96%, and F1-score of 96% and 97% for Benign and Malignant tumors, respectively. Therefore, the proposed MIST-EBCD-MCANN framework is a highly reliable and effective solution for the early detection of breast cancer in its early stages.</p>