Classification of Breast Cancer Using Deep CNN: A Comparative Analysis

Currently, several approaches have emerged to facilitate the timely identification of breast cancer. Detecting breast cancer prior facilitates appropriate treatment. Machine learning (ML) plays an important role in this domain by identifying and categorizing breast cancers. This article explores a new methodology for detecting and classifying breast carcinoma using deep convolutional neural networks (CNN) and conducts a comparative analysis. The study involves data preprocessing, constructing a CNN framework with multiple layers, training the model, and evaluating its accuracy and recall using performance metrics. Various epochs are considered, and the accuracy values are compared. The results indicate that the suggested system exhibits higher precision in categorizing and identifying breast cancer in contrast to conventional approaches. The application of CNNs for breast cancer classification has yielded the desired outcomes. Furthermore, this paper highlights the need for further research in this field, which could potentially lead to the development of advanced treatments to reduce mortality rates.