Reviewing the Landscape of Deep Learning Approaches for Breast Cancer Detection

Rapid and precise identification of breast cancer is crucial in order to improve patient outcomes for this prevalent and life-threatening disease. The emergence of AI and deep learning techniques has opened new avenues for enhancing breast cancer screening and diagnosis. CNNs and ensemble models, among other deep learning architectures, have demonstrated promising results in the analysis of medical pictures and the extraction of useful characteristics for precise diagnosis. Additionally, the use of transfer learning and pretrained CNN models has demonstrated superior performance in classifying breast tumor images. Studies investigating the usage of one-dimensional data conversion into pictures and utilizing stacked ensemble models for increased classification accuracy show that the use of deep learning extends beyond image-based research. These techniques offer potential solutions for addressing the challenges in breast cancer detection, such as distinguishing between benign and malignant tumors and predicting prognosis. By automating the diagnosis process, deep learning algorithms can assist medical professionals in making informed decisions and providing personalized treatment plans. Enhancing the accuracy and reliability of breast cancer prediction models necessitates further research into the incorporation of larger datasets, processing multi-modal data, and advancements in image generation methods. Overall, AI and deep learning hold great promise in revolutionizing breast cancer screening and diagnosis, ultimately leading to improved survival rates and better patient care.