Breast cancer is considered one of the most dangerous types of cancer that affect women around the world, and high incidence rates of this disease pose a significant challenge to the medical community. To combat this disease, many different techniques and tools have been developed for early detection of breast cancer, including the Convolutional Neural Network (CNN) technique.<br /><br />The CNN technique is a modern technique in the field of machine learning that is used to analyze images and videos in a networked manner similar to the human brain. Recently, this technique has been used to diagnose breast cancer, where CNN was trained on a large set of breast medical images to accurately identify cancerous tumors.<br /><br />The CNN technique relies on a set of different networked layers, where the initial layers analyze the image and extract initial information from it, and subsequent layers analyze this information more deeply and extract more accurate information until cancerous tumors are accurately identified.<br /><br />The CNN technique is one of the modern techniques that rely on deep learning, a concept that means the system learns automatically from the data it is trained on and uses this learning to improve diagnostic accuracy and better identify cancerous tumors.<br /><br />Several studies have shown that the CNN technique is capable of accurately identifying cancerous tumors and outperforms traditional techniques used in breast cancer diagnosis. Additionally, using this technique helps improve treatment and recovery opportunities for breast cancer by diagnosing the disease early and accurately.<br /><br />It is important to note that the CNN technique is not a complete substitute for traditional medical diagnosis but rather an important addition to the tools and techniques used in breast cancer diagnosis. Using this technique requires training doctors and specialists in the field of medical imaging to use it and understand its results correctly.<br /><br />In conclusion, the CNN technique is an important achievement in the field of breast cancer diagnosis, as it helps improve diagnostic accuracy and accurately identify cancerous tumors. It is expected that this technique will continue to evolve and improve in the future, leading to better treatment and recovery opportunities for breast cancer.