An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning

Abstract

Background: Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids in mitigating the disease’s complexities and curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of the false-positive findings, which reduces the patient’s survival chances. Consequently, approaches that learn to discern breast masses can reduce the number of misconceptions and incorrect diagnoses. Conventionally used classification models focus on feature extraction techniques specific to a particular problem based on domain information. Deep learning strategies are becoming promising alternatives to solve the many challenges of feature-based approaches. Methods: This study introduces a convolutional neural network (ConvNet)-based deep learning method to extract features at varying densities and discern mammography’s normal and suspected regions. Two different experiments were carried out to make an accurate diagnosis and classification. The first experiment consisted of five end-to-end pre-trained and fine-tuned deep convolution neural networks (DCNN). The in-depth features extracted from the ConvNet are also used to train the support vector machine algorithm to achieve excellent performance in the second experiment. Additionally, DCNN is the most frequently used image interpretation and classification method, including VGGNet, GoogLeNet, MobileNet, ResNet, and DenseNet. Moreover, this study pertains to data cleaning, preprocessing, and data augmentation, and improving mass recognition accuracy. The efficacy of all models is evaluated by training and testing three mammography datasets and has exhibited remarkable results. Results: Our deep learning ConvNet+SVM model obtained a discriminative training accuracy of 97.7% and validating accuracy of 97.8%, contrary to this, VGGNet16 method yielded 90.2%, 93.5% for VGGNet19, 63.4% for GoogLeNet, 82.9% for MobileNetV2, 75.1% for ResNet50, and 72.9% for DenseNet121. Conclusions: The proposed model’s improvement and validation are appropriated in conventional pathological practices that conceivably reduce the pathologist’s strain in predicting clinical outcomes by analyzing patients’ mammography images.

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

“…Recorded results showed that, their proposed ConvNet + SVM model attained a training accuracy of 97.7% with validating accuracy of 97.8%. Furthermore, pre-trained and fine-tuned VGGNet16 model acquired accuracy of 90.2%, VGGNet 93.5%, GoogLeNet 63.4%, MobileNetV2 82.9%, ResNet50 75.1%, and DenseNet121 72.9% [ 19 ]. Other authors [ 20 ] combined various convolution and capsule features in a feature fusion method to derive an updated feature set.…”

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

“…The hyperparameters used for training are described in Table 3. Automatic detection and extraction of MC ROI from a mammogram is a challenging task for the CAD system [53]. Previously, most clinicians extracted ROI manually, and artificial factors, such as fatigue and extensive mammogram interpretation, reduced ROI diagnostic accuracy [57].…”

“…SVM is a machine learning method based on the structural risk minimization theory, which has an advantage in pattern recognition, regression analysis, and other fields [ 53 ]. SVM classifiers aim to construct a hyperplane to offer an efficient and accurate method for diagnosing and categorizing mammogram images.…”

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

“…The misdetections exhibit a soft spot in the classifier; it could confuse benign microcalcifications as malignant and benign masses as malignant but not in the opposite case. As such, the correct detection and classification of the lesions that can occur in a breast is not an easy task [20,31]. False negatives occur in grayish mammograms associated with dense breast tissue, which could be reduced by implementing CLAHE image enhancement as it dramatically improves areas with low contrast.…”