Classification of Breast Cancer Ultrasound Images with Deep Learning-Based Models

Breast cancer is the type of cancer that affects women the most frequently in the world. Additionally, it is the biggest cause of death for women. For the detection and treatment of breast cancer, there are numerous imaging techniques. For medical image analysts, making a diagnosis is arduous, time-, routine, consuming and tedious. Additionally, the growing volume of ultrasounds to interpret has overloaded practitioners and analysts. In the past, researches have been done with mammogram images. The research aims to take a different approach. The hypothesis is that by using artificial intelligence (AI) for ultrasound analysis, the process of computer-aided diagnosis (CAD) can be made, effective, interesting and free from subjectivity. Research's purpose is to classify benign (non-cancerous), malignant (cancerous), and normal samples. The dataset contains 780 images in total. Data were split %70 for training and %30 for validation. In this dataset, data augmentation and data preprocessing are also applied. Three models are used to classify samples. While ResNet50 scores %85.4 accuracy, ResNeXt50 scores %85.83, VGG16 scores %81.11. Making the diagnosis by artificial intelligence will provide relief in the field of medicine. Computer vision models may be used in medicine. Therefore, providing more data and testing data more broadly will improve the model.