BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

SIMPLE SUMMARY: Breast cancer diagnosis at an early stage using mammography is important, as it assists clinical specialists in treatment planning to increase survival rates. The aim of this study is to construct an effective method to classify breast images into four classes with a low error rate. Initially, unwanted regions of mammograms are removed, the quality is enhanced, and the cancerous lesions are highlighted with different artifacts removal, noise reduction, and enhancement techniques. The number of mammograms is increased using seven augmentation techniques to deal with over-fitting and under-fitting problems. Afterwards, six fine-tuned convolution neural networks (CNNs), originally developed for other purposes, are evaluated, and VGG16 yielded the highest performance. We propose a BreastNet18 model based on the fine-tuned VGG16, changing different hyper parameters and layer structures after experimentation with our dataset. Performing an ablation study on the proposed model and selecting suitable parameter values for preprocessing algorithms increases the accuracy of our model to 98.02%, outperforming some existing state-of-the-art approaches. To analyze the performance, several performance metrics are generated and evaluated for every model and for BreastNet18. Results suggest that accuracy improvement can be obtained through image pre-processing techniques, augmentation, and ablation study. To investigate possible overfitting issues, a k-fold cross validation is carried out. To assert the robustness of the network, the model is tested on a dataset containing noisy mammograms. This may help medical specialists in efficient and accurate diagnosis and early treatment planning. ABSTRACT: Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.