Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division

Abstract

Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations.

NSBR-Net: A Novel Noise Suppression and Boundary Refinement Network for Breast Tumor Segmentation in Ultrasound Images

“…Compared to ultrasound, mammography is irreplaceable due to its ability to clearly detect tiny calcifications within breast tissue. Thus, mammography is also a commonly employed method for breast cancer screening, with a wealth of AI-related research conducted in this area, including machine learning methods [39] and deep learning methods [40]. Digital Breast Tomosynthesis (DBT), which significantly mitigates the issue of missed detections caused by overlapping breast fibroglandular tissue in mammography, has also emerged as a widely adopted new technology.…”

Annotation-free deep-learning framework for microcalcifications detection on mammograms

“…Due to their tiny sizes (e.g., 100 × 100 pixels in 4000 × 3000 images), 3 keeping the high semantic details of the lesion is crucial to efficiently identify their morphology and associate a risk of malignancy. [16][17][18] On the contrary, degrading pixel information may lead to the creation of noise or bright artifacts that can be confused with MCs.…”

“…18 Sakaida et al designed a 224 × 224 patch classifier to capture MC's presence and tested various ResNet architectures. 16 Finally, Quintana et al have studied the impact of patch sizes and MG resolutions on the classification of breast lesions, including masses and calcifications. The authors conclude that a single size or resolution is not optimal for catching all lesions.…”

OrthoDETR: A Streamlined Transformer-Based Approach for Precision Detection of Orthopedic Medical Devices

“…For instance, Mathesul and others proposed a deep learning method based on CNNs to enhance the detection of COVID-19 and its variants from chest X-ray images [18]. Furthermore, Sakaida and colleagues developed a method for detecting breast calcifications using deep learning, capable of classifying the presence of calcifications in the breast [19].…”