Improving Mass Detection in Mammography Using Focal Loss Based RetinaNet

Abstract

Breast cancer is a significant global health issue and plays a crucial role in improving patient outcomes through early detection. This study aims to enhance the accuracy and efficiency of breast cancer diagnosis by investigating the application of the RetinaNet and Faster R-CNN algorithms for mass detection in mammography images. A specialized dataset was created for mass detection from mammography images and validated by an expert radiologist. The dataset was trained using RetinaNet and Faster R-CNN, a state-of-the-art object detection model. The training and testing were conducted using the Detectron2 platform. To avoid overfitting during training, data augmentation techniques available in the Detectron2 platform were used. The model was tested using the AP50, precision, recall, and F1-Score metrics. The results of the study demonstrate the success of RetinaNet in mass detection. According to the obtained results, an AP50 value of 0.568 was achieved. The precision and recall performance metrics are 0.735 and 0.60 respectively. The F1-Score metric, which indicates the balance between precision and recall, obtained a value of 0.66. These results demonstrate that RetinaNet can be a potential tool for breast cancer screening and has the potential to provide accuracy and efficiency in breast cancer diagnosis. The trained RetinaNet model was integrated into existing PACS (Picture Archiving and Communication System) systems and made ready for use in healthcare centers.

Keywords

• Breast cancer
• RetinaNet
• Mass detection
• Focal loss
• Detectron2
• Object detection

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