An Improved Of Deep Convolutional Neural Network For Early Breast Cancer Detection

Breast cancer is a huge global health challenge, ranking as the second-largest cause of mortality worldwide. Early identification is crucial for effective treatment and better survival rates. Various screening methods, including mammography, ultrasonography, and thermography, have been developed to aid in the early diagnosis of breast cancer. Leveraging image processing techniques and deep learning algorithms, radiologists can boost the accuracy of detecting breast problems. This paper suggests the use of an upgraded Deep Convolutional Neural Network (DCNN) for the early and accurate identification and diagnosis of breast cancer. Setting itself apart from earlier approaches, this work leverages a DCNN with 12 layered processing layers, leading to better detection and diagnosis accuracy. The Mini Mammographic Database (MIAS) serves as the dataset for testing the proposed system's performance. Results reveal that the Deep CNN achieves exceptional accuracy, reaching 99.1%. Furthermore, comparative analysis against related research demonstrates the advantages of the proposed DCNN-based technique. This research contributes to expanding the area of breast cancer diagnosis and emphasises the potential of deep learning to enhance diagnosis and improve.