A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images

Kriti, .; Virmani, Jitendra; Agarwal, Ravinder

doi:10.1007/s11831-021-09620-8

Cited by 7 publications

(1 citation statement)

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“…As a result, ultrasound does not use ionizing radiation, which is a signifcant beneft [3]. Te difculty in interpreting ultrasound images is due to speckle and low contrast despite the numerous benefts that may be gained from using ultrasound [5]. Tis causes the images to be degraded, even though the numerous benefts ultrasound can gain.…”

Section: Introductionmentioning

confidence: 99%

An Efficient USE-Net Deep Learning Model for Cancer Detection

Almutairi

Manimurugan

Aborokbah

et al. 2023

International Journal of Intelligent Systems

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Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE-Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1-score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1-score for mammogram images.

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Section: Introductionmentioning

confidence: 99%