Breast Cancer Detection Based on Convolutional Neural Networks
Revanth Reddy Mannem, Suraj Bhyri · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: Breast cancer remains a critical health concern, demanding early detection and accurate classification for effective treatment. In this research, we conduct a comparative study between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We begin by curating a comprehensive dataset of breast cancer images and apply appropriate data preprocessing techniques for optimal model input. The dataset is divided into training, validation, and testing sets to evaluate model performance. The CNN model is constructed with multiple convolutional and pooling layers, followed by fully connected layers for classification. Meanwhile, the DenseNet121, a powerful pre-trained model, is fine-tuned for breast cancer detection. Through rigorous evaluation, we assess both models using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. Our results demonstrate that the DenseNet121 outperforms the custom CNN model, achieving higher accuracy and reliability in identifying and classifying breast cancer. To ensure wider accessibility, we integrate the superior DenseNet121 model into a user-friendly web-based interface using Python Flask. This interface empowers medical professionals and the general public to perform real-time breast cancer predictions with ease. Ethical considerations are paramount, ensuring data privacy, security, and transparency in all model predictions. In conclusion, our comparative study highlights the superiority of the pre-trained DenseNet121 model over a custom CNN for breast cancer detection and classification. By leveraging advanced deep learning techniques and a user-friendly interface, our research contributes to improved breast cancer diagnosis and patient care on a broader scale