Usually, screening (mostly mammography) is used by radiologists to manually detect breast cancer. The likelihood of identifying suspected cases as false positives or false negatives is significant, contingent on the experience of the radiologist and the kind of imaging screening device/method utilized. The confirmation of the type of tumour seen by the radiologist is sent for histological investigation (microscopic analysis) through a biopsy, where the tumor's grade and stage, which are used in the latter stages of treatment, are ascertained through biopsy. However, a secondary issue with the cancer detection process is that only 15 to 30% of instances that are referred for biopsy result in malignant findings. Since deep learning demonstrated remarkable performance in visual recognition challenges, it has been widely applied to a variety of tasks. Similar examples include deep learning applications in healthcare, which are gaining a lot of interest from the research community. Deep learning is used to identify, categories tumours, and breast cancer is a significant global health concern. The medical sciences could now make more accurate diagnoses and detections due to recent advancements in machine learning techniques. Hence due to systems potential accuracy, it could offer optimistic outcomes when used to read malignant images. In imaging domains, deep learning-based methods have achieved remarkable success in constituent segmentation (UNet), localization (DenseNet), and classification (VGG-19). This study examines, how deep learning methods are assisting in the highly accurate diagnosis of benign or malignant tumours based on screened images. In contrast to a mammogram, which is covered in detail, this paper briefly discusses imaging methods for cancer detection. Early detection and cost effectiveness are two main benefits of applying machine learning and deep learning techniques to mammograms.

通常，放射科医生使用筛查（主要是乳房X光检查）来手动检测乳腺癌。将疑似病例识别为假阳性或假阴性的可能性很大，具体取决于放射科医生的经验以及所使用的成像筛查设备/方法的类型。放射科医生所看到的肿瘤类型的确认将通过活检进行组织学研究（显微镜分析），通过活检确定肿瘤的等级和分期，用于治疗的后期阶段。然而，癌症检测过程的一个次要问题是，只有 15% 至 30% 的活检结果呈恶性。由于深度学习在视觉识别挑战中表现出卓越的性能，因此已广泛应用于各种任务。类似的例子包括医疗保健领域的深度学习应用，这些应用引起了研究界的广泛兴趣。深度学习用于识别肿瘤、对肿瘤进行分类，而乳腺癌是一个重大的全球健康问题。由于机器学习技术的最新进展，医学现在可以做出更准确的诊断和检测。因此，由于系统潜在的准确性，它在用于读取恶性图像时可以提供乐观的结果。在成像领域，基于深度学习的方法在成分分割（UNet）、定位（DenseNet）和分类（VGG-19）方面取得了显着的成功。这项研究探讨了深度学习方法如何根据筛选的图像协助高度准确地诊断良性或恶性肿瘤。与详细介绍的乳房X光检查相比，本文简要讨论了癌症检测的成像方法。早期检测和成本效益是将机器学习和深度学习技术应用于乳房X光检查的两个主要好处。