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

Biology

Friso De Boer¹  Sami Azam¹  Mirjam Jonkman¹  Pronab Ghosh²  Abul Kalam Muhammad Rakibul Haque Rafid³  Sidratul Montaha³  Md. Zahid Hasan³ 

[1] College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT 0909, Australia;Department of Computer Science (CS), Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada;Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh;

关键词: mammograms;    image preprocessing;    fine-tuned VGG16;    deep learning;    breast cancer classification;    data augmentation;   

DOI  :  10.3390/biology10121347

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown