A Systematic Analysis Using DNN Based Breast Cancer Identification and Stage Classification Model

Abstract

The increased incidence of breast cancer on a global level is an important problem in public health, emphasizing the need for rapid and effective techniques for diagnosing the cancer at its earliest stages. This study offers an innovative Deep Neural Network (DNN) technique for identifying and categorizing breast cancer. It utilizes advanced methods of machine learning to improve its precision and efficacy. This study offers an in-depth examination of DNNs for their use of detecting breast cancer. This work concentrates on the DNNs' ability to identify complicated patterns within mammographic images, with the goal of enhancing the accuracy of detection. The proposed DNN design incorporates several levels of abstraction, taking use of the organization possibilities of neural networks. Convolutional layers collect local data, whereas densely associated layers capture global patterns, allowing the algorithm to identify subtle problems that indicate tumors in breasts. The design of the model is optimized by further training on different data sets, guaranteeing its ability to respond to the inherent variations in tissue makeup and lesion characteristics. To attempt to address the challenges related to a lack of data annotations, transfer learning techniques are employed. These methods leverage the knowledge gained from models that have been trained on large datasets. The success rate of the proposed DNN model for identifying and categorizing breast cancer is thoroughly assessed on standard datasets, through assessing its performance with conventional methods and recent algorithms. The model's superior in providing reliable and precise diagnostic results can be seen through the use of assessment standards like the accuracy and confusion matrix. The model suggested has strong diagnostic accuracy, offering an appropriate strategy for early and efficient identification of breast cancers.