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dc.contributor.author Montaha, Sidratul
dc.contributor.author Azam, Sami
dc.contributor.author Muhammad Rakibul Haque Rafid, Abul Kalam
dc.contributor.author Ghosh, Pronab
dc.contributor.author Hasan, Md. Zahid
dc.contributor.author Jonkman, Mirjam
dc.contributor.author De Boer, Friso
dc.date.accessioned 2022-03-06T04:14:10Z
dc.date.available 2022-03-06T04:14:10Z
dc.date.issued 2021-11-13
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7410
dc.description.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 over fitting 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. en_US
dc.language.iso en_US en_US
dc.publisher Biology en_US
dc.subject Mammograms en_US
dc.subject Image preprocessing en_US
dc.subject Fine-tuned en_US
dc.subject VGG16 en_US
dc.subject Deep learning en_US
dc.subject Breast cancer classification en_US
dc.subject Data augmentation en_US
dc.subject Ablation study en_US
dc.subject Transfer learning models en_US
dc.subject Feature map analysis en_US
dc.subject CBIS-DDSM en_US
dc.title Breastnet18 en_US
dc.title.alternative a High Accuracy Fine-tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images en_US
dc.type Article en_US


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