Breast cancer: toward an accurate breast tumor detection model in mammography using transfer learning techniques

Female breast cancer has now surpassed lung cancer as the most common form of cancer globally. Although several methods exist for breast cancer detection and diagnosis, mammography is the most effective and widely used technique. In this study, our purpose is to propose an accurate breast tumor detection model as the first step into cancer detection. To guarantee diversity and a larger amount of data, we collected samples from three different databases: the Mammographic Image Analysis Society MiniMammographic (MiniMIAS), the Digital Database for Screening Mammography (DDSM), and the Chinese Mammography Database (CMMD). Several filters were used in the pre-processing phase to extract the Region Of Interest (ROI), remove noise, and enhance images. Next, transfer learning, data augmentation, and Global Pooling (GAP/GMP) techniques were used to avoid imagery overfitting and to increase accuracy. To do so, seven pre-trained Convolutional Neural Networks (CNNs) were modified in several trials with different hyper-parameters to determine which ones are the most suitable for our situation and the criteria that influenced our results. The selected pre-trained CNNs were Xception, InceptionV3, ResNet101V2, ResNet50V2, ALexNet, VGG16, and VGG19. The obtained results were satisfying, especially for ResNet50V2 followed by InceptionV3 reaching the highest accuracy of 99.9%, and 99.54% respectively. Meanwhile, the remaining models achieved great results as well, proving that our approach starting from the chosen filters, databases, and pre-trained models with the fine-tuning phase and the used global pooling technique is effective for breast tumor detection. Furthermore, we also managed to determine the most suitable hyper-parameters for each model using our collected dataset.