Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska, Zuzanna; Theek, Benjamin; Darguzyte, Milita; Palmowski, Moritz; Stickeler, Elmar; Schulz, Volkmar; Kießling, Fabian

doi:10.3390/cancers14020277

Cited by 14 publications

(10 citation statements)

References 60 publications

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“…At the same time, there was the coexistence of benign and malignant lesions, which included numerous hyperplasia lesions. The detection rate of type 2 and 3 lesions in this study was lower, but it was significantly higher than that in previous studies ( 32 , 35 – 37 ).…”

Section: Discussioncontrasting

confidence: 93%

Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Zhang

Tong

et al. 2022

Front. Oncol.

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ObjectiveThis study aimed to evaluate a convolution neural network algorithm for breast lesion detection with multi-center ABUS image data developed based on ABUS image and Yolo v5.MethodsA total of 741 cases with 2,538 volume data of ABUS examinations were analyzed, which were recruited from 7 hospitals between October 2016 and December 2020. A total of 452 volume data of 413 cases were used as internal validation data, and 2,086 volume data from 328 cases were used as external validation data. There were 1,178 breast lesions in 413 patients (161 malignant and 1,017 benign) and 1,936 lesions in 328 patients (57 malignant and 1,879 benign). The efficiency and accuracy of the algorithm were analyzed in detecting lesions with different allowable false positive values and lesion sizes, and the differences were compared and analyzed, which included the various indicators in internal validation and external validation data.ResultsThe study found that the algorithm had high sensitivity for all categories of lesions, even when using internal or external validation data. The overall detection rate of the algorithm was as high as 78.1 and 71.2% in the internal and external validation sets, respectively. The algorithm could detect more lesions with increasing nodule size (87.4% in ≥10 mm lesions but less than 50% in <10 mm). The detection rate of BI-RADS 4/5 lesions was higher than that of BI-RADS 3 or 2 (96.5% vs 79.7% vs 74.7% internal, 95.8% vs 74.7% vs 88.4% external). Furthermore, the detection performance was better for malignant nodules than benign (98.1% vs 74.9% internal, 98.2% vs 70.4% external).ConclusionsThis algorithm showed good detection efficiency in the internal and external validation sets, especially for category 4/5 lesions and malignant lesions. However, there are still some deficiencies in detecting category 2 and 3 lesions and lesions smaller than 10 mm.

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Section: Discussioncontrasting

confidence: 93%

Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Zhang

Tong

et al. 2022

Front. Oncol.

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“…An analysis was conducted to compare the radionic signature (RS) derived from manually produced segments and the RS acquired from detection devices. YOLOv3, which underwent training using logarithmic derivatives of US images, demonstrates enhanced performance in the detection of breast lesions 38 . An automated data-driven model based on the YOLO algorithm is utilized to diagnose breast cancer in mammography.…”

Section: Cnn and Ml-based Modelsmentioning

confidence: 99%

Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor

Mahmood,

Aminfar

2024

Bio. Med. Tar. Jour.

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The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems.

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“…Te CAD system was created [11] to detect and classify breast lesions as benign or malignant. In the preprocessing step, additional data were added and spatial alterations were carried out, both of which were done to construct breast lesion identifcation.…”

Section: Related Workmentioning

confidence: 99%

“…In (11), the term denoted by D scale (v d , t d ) refects the multiplication of channel-wise that occurs among the feature maps denoted by v d ∈ Z P×Q and the scalar denoted by t d ∈ [0, 1]. Te layer of S.E.…”

Section: Quadrants Dynamic Histogram Equalization (Qdhe)mentioning

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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Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Cited by 14 publications

References 60 publications

Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor

An Efficient USE-Net Deep Learning Model for Cancer Detection

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