article · IEEE Access
Breast cancer is a primary cause of cancer death among women, and early detection significantly increases survival rates. This research developed a novel deep-learning model, employing a transfer-learning technique, for the automatic detection and diagnosis of suspected breast cancer areas from mammographic images. The model extracted features from the MIAS dataset using various pre-trained convolutional neural networks, including VGG16. Experimental results demonstrated that the VGG16 model, when enhanced with transfer learning, achieved high performance in classifying mammogram images, showing strong accuracy, sensitivity, specificity, precision, F-score, and AUC values across different evaluation methods.
Early and accurate detection of breast cancer is vital for effective treatment and patient survival. This research offers a promising automated method using artificial intelligence to identify suspected areas in mammograms, potentially aiding medical professionals and improving diagnostic efficiency.
This research presents an early-stage artificial intelligence tool for medical imaging analysis. It could assist radiologists and oncologists in the automatic detection and diagnosis of breast cancer from mammograms, potentially speeding up the diagnostic process and improving accuracy. Further validation and integration into clinical workflows would be required for real-world use.
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Breast cancer (BC) is one of the primary causes of cancer death among women. Early detection of BC allows patients to receive appropriate treatment, thus increasing the possibility of survival. In this work, a new deep-learning (DL) model based on the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection and diagnosis of the BC suspected area based on two techniques namely 80-20 and cross-validation. DL architectures are modeled to be problem-specific. TL uses the knowledge gained during solving one problem in another relevant problem. In the proposed model, the features are extracted from the mammographic image analysis- society (MIAS) dataset using a pre-trained convolutional neural network (CNN) architecture such as Inception V3, ResNet50, Visual Geometry Group networks (VGG)-19, VGG-16, and Inception-V2 ResNet. Six evaluation metrics for evaluating the performance of the proposed model in terms of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC curve (AUC) has been chosen. Experimental results show that the TL of the VGG16 model is powerful for BC diagnosis by classifying the mammogram breast images with overall accuracy, sensitivity, specificity, precision, F-score, and AUC of 98.96%, 97.83%, 99.13%, 97.35%, 97.66%, and 0.995, respectively for 80-20 method and 98.87%, 97.27%, 98.2%, 98.84%, 98.04%, and 0.993 for 10-fold cross-validation method.
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DOI: 10.1109/access.2021.3079204
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