ArticleHealthcare (Basel, Switzerland)2022
Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 28 citations in OpenAlex.
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI.BMC medical imaging · 2024Article
- The efficacy of machine learning models in lung cancer risk prediction with explainability.PloS one · 2024Article
- Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network.Scientific reports · 2023Article
- Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
Abstract
Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.