BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

Abstract

The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

“…Over the past two decades, extensive research has been conducted to develop effective approaches for achieving precise segmentation [13][14][15][16]. Deep learning (DL) models, predominantly convolutional neural networks (CNNs), have proven remarkably successful in accurately segmenting anatomical structures and identifying pathological regions in various medical imaging modalities, including X-ray, MRI, CT, and ultrasound [17][18][19][20][21][22][23][24][25][26].…”

BCUIS‐Net: A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion

“…Computer‐aided diagnosis (CAD) provides a pathway to address this challenge, which image segmentation is a key step within CAD, directly affecting the accuracy of the diagnosis 6 . However, the segmentation of breast cancer ultrasound images is challenging due to the low contrast of ultrasound image, the presence of strong shadows and ill‐defined borders between the lesioned and normal breast areas, especially for irregular‐shaped breast lesions 7,8 …”

“…6 However, the segmentation of breast cancer ultrasound images is challenging due to the low contrast of ultrasound image, the presence of strong shadows and ill-defined borders between the lesioned and normal breast areas, especially for irregular-shaped breast lesions. 7,8 Recently, the implementation of deep learning methods for breast lesion detection from ultrasound images has received considerable interest. 9 In particular, conventional semantic segmentation networks, including fully convolutional networks (FCNs) 10 and U-Net, 11 have been proved to be effective approaches for accurate detection and delineation of breast lesions.…”