Analysis of Breast Cancer Classification Using Deep CNN with Adaptive Learning Rate

Different methodologies have been discovered for the diagnosis of breast cancer in early stage with increasing technology. This study examines a novel method for detecting and categorizing breast cancer using convolutional neural networks (CNN). The process involves preprocessing the data, constructing the CNN architecture with different layers, training the model on a significant dataset, and evaluating its accuracy and recall through performance metrics. The outcomes revealed that the proposed system gained higher accuracy in classifying and detecting breast cancer as compared to the traditional methods. This article highlights the potential use of unique techniques to enhance the accuracy of breast cancer diagnosis and highlights the need for further research in this field, which can eventually result in the development of new and advanced treatments to reduce the mortality rates.