Breast cancer remains one of the leading causes of mortality among women worldwide. Early and accurate diagnosis through medical imaging can significantly improve patient outcomes. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown promising results in classifying medical images with high accuracy. However, the performance of these models often depends on appropriate data preprocessing techniques. This paper investigates the efficacy of using Min-Max Normalization combined with a CNN-based architecture to classify breast cancer images. Experimental results demonstrate that applying Min-Max Normalization prior to training not only enhances model convergence but also improves classification accuracy and robustness. These findings suggest that the proposed approach can provide a reliable diagnostic tool for clinicians in the early detection of breast cancer. This feature matrix is used as input for the pretrained model and convolutional neural network. Pre-trained models such as VGG16 and VGG19 are investigated using the concept of transfer learning. The framework's structure consists of 14 layers in total. In order to optimize the classification accuracy, the hyperparameters are changed. An ideal accuracy of 93.9% is attained by the convolutional neural network architecture that was created.
Convolutional Neural Network, Breast Cancer, VGG16, VGG19, Min-Max Normalization
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