Early Detection of Breast Cancer Using Gan and Resnet Emulsion Approach
Keywords:
Breast cancer detection, Generative Adversarial Network (GAN), Residual Neural Networks, Convolutional Neural Networks, Machine Learning.Abstract
Breast cancer analysis is critical for clinical diagnosis and treatment. It is characterized by abnormal cell growth and tumor formation and remains a significant health concern among women. To address this issue, researchers have explored traditional and various individual deep learning algorithms, such as convolutional neural networks (CNNs) and artificial neural networks (ANNs). Furthermore, the mono-based model study could cause the model's classification accuracy to be low when using deep learning in medical diagnosis. So, to address this issue, we proposed a novel approach that combines generative adversarial networks (GANs) with residual neural networks (ResNet) for accurate breast cancer detection using histopathological images. The enriched dataset ensures better generalization during training, and we used GANs to supplement the training dataset. By generating synthetic images, GANs enhance feature recognition and improve model robustness. The augmented data fine-tunes ResNet, a powerful deep learning architecture, for classification. The GAN-ResNet channel uses discriminatory features extracted by the discriminator from GAN-generated images. This fusion combines GANs’ discriminative power with ResNet’s classification capabilities. We specifically fine-tuned the final model layer for binary classification, enabling it to distinguish between malignant and benign breast tissue. We adapt the loss function to handle imbalances in the medical dataset, ensuring a more robust and accurate model. Our proposed model demonstrates a remarkable 95% accuracy in analyzing histopathological images, validating its efficacy for early breast cancer detection at its earliest stage.
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