Abstract
Breast cancer is known to be common in many developed countries. It is reported as the most common type of cancer in the US, affecting one in eight women. In Korea, thyroid cancer is the most common type of cancer, followed by breast cancer in women. Considering this, early detection and accurate diagnosis of breast cancer are crucial for reducing the associated death rate. Recently, cancer diagnosis systems using medical images have attracted significant attention. Medical imaging methods, such as computed tomography and magnetic resonance imaging, can reveal the overall shape, heterogeneity, and growth speed of carcinoma and are, thus, more commonly employed for diagnoses. Medical imaging has gained popularity since a recent study identified that it could reflect the gene phenotype of a patient. However, an aided diagnosis system based on medical images requires high-specification equipment to analyze high-resolution data. Therefore, this article proposes an edge extraction algorithm and a modified convolutional recurrent neural network (CRNN) model to accurately assess breast cancer based on medical imaging. The proposed algorithm extracts line-segment information from a breast mass image. The extracted line segments were classified into 16 types. Each type was uniquely labeled and compressed. The image compressed in this process was used as the input for the modified CRNN model. Traditional deep learning models were used to evaluate the performance of the proposed algorithm. The results show that the proposed model had the highest accuracy and lowest loss (99.75% and 0.0257, respectively).
Highlights
Cancer is one of the most dangerous diseases affecting humankind
The extended line-segment feature analysis (eLFA)-convolutional recurrent neural network (CRNN), VGG-M, and CRNN models demonstrated the highest accuracy of 100%, followed by the VGG-S (98%), AlexNet (59%), and VGG-F (41%) models
The proposed eLFA-CRNN, VGG-M, and CRNN models produced accuracies of 100%, followed by the VGG-S (64%), and VGG-F and AlexNet (60%) models
Summary
Cancer is one of the most dangerous diseases affecting humankind. It indicates a condition of the body in which the proliferation and inhibition control of cells, the smallest unit of the human body, works abnormally, destroying normal tissues. As the CNN-based image classification model is used, direction, contour, and spatial information are collected through a convolution operation in the ROI region and used for prediction and diagnosis. We classify points, lines, and planes, which are the basic elements that make up an image, into types, and count and reduce them This was devised from the CNN model, an image classification neural model, in which the convolution layer extracts contours, directions, and spatial information about the input through parallel arithmetic of various kernels with the input images.
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