Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Falconi, Lenin G.; Pérez, María Jesús Verdú; Aguila, Wilbert G.; Conci, Aura

doi:10.25046/aj050220

Cited by 50 publications

(31 citation statements)

References 37 publications

(59 reference statements)

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“…The primary goal of this study is to create a model that can effectively detect and classify breast tumors while also minimizing FPR and FNR rates and increasing MCC values. The CBIS-DDSM [ 26 ] dataset is utilized for this. As seen in Figure 1 , the initial stage is to remove the rough white borders, followed by the removal of artifacts and pectoral muscles.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The results achieved using this method are 93% sensitivity for OMI-H, 91% sensitivity for OMI-GE, and 99% sensitivity for INbreast but FPR and FNR rates for OMI-GE dataset increases to 12% and 20%, for INbreast dataset increases to 20% and 29%, and FNR rate for OMI-H increases to 13%, respectively. In this paper [ 26 ], an author proposed a method to classify breast cancer tumors using the VGG-16 network on the CBIS-DDSM dataset. This method achieved 82% accuracy, but the drawback is that their MCC value is 63%, FPR is high, 22%, and FNR is 15%.…”

Section: Related Workmentioning

confidence: 99%

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Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Data augmentation techniques are also a well-known strategy to expand the diversity of the dataset, improving the robustness of the model and contributing to the overall prediction accuracy which has been proven its worth in the field of deep learning. This technique can reduce overfitting in CNN models [ 49 ] as the model will have enough distinct data to train on and can also accelerate convergence. The most commonly applied data transformation techniques are flipping, rotating, zooming, mirroring, and cropping.…”

Section: Data Augmentationmentioning

confidence: 99%

“…Unfortunately, this also applies to publicly available, breast cancer datasets. To deal-with this concern, transfer learning and fine-tuning can help to increase the accuracy of classifiers by transferring knowledge from another domain where large datasets are available [ 49 ]. Transfer learning of a CNN model that is pre-trained with a large scale natural image dataset (i.e., ImageNet), to medical images [ 51 ] can be a promising approach.…”

Section: Proposed Modelmentioning

confidence: 99%

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

Montaha

Azam

Rafid

et al. 2021

Biology

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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.

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Detection of Breast Cancer in Mammography Using Pretrained Convolutional Neural Networks with Fine-Tuning

Muñoz-Chavez,

Sánchez-Cruz,

Sossa-Azuela

et al. 2023

Transactions on Computational Science and Computational Intelligence

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Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Cited by 50 publications

References 37 publications

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

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

Detection of Breast Cancer in Mammography Using Pretrained Convolutional Neural Networks with Fine-Tuning

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