ReviewSensors (Basel, Switzerland)2018
Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Skin Temperature of Acupoints in Health and Disease: A Systematic Review.Journal of integrative and complementary medicine · 2022Pooled it
- Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review.Journal of medical systems · 2020Pooled it
- No association between abortion and risk of breast cancer among nulliparous women: Evidence from a meta-analysis.Medicine · 2020Pooled it
- A method for the assessment of rheumatoid arthritis using neural network supported static and dynamic thermal analysis.Physical and engineering sciences in medicine · 2026Article
- Lightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction.Scientific reports · 2026Article
- Artificial Intelligence-Based Wearable Sensing Technologies for the Management of Cancer, Diabetes, and COVID-19.Biosensors · 2025Review
- Graphene-based nanozymes for revolutionizing biomedical research.RSC advances · 2025Review
- Lightweight Statistical and Texture Feature Approach for Breast Thermogram Analysis.Journal of imaging · 2025Article
- Dual-Tree Complex Wavelet Pooling and Attention-Based Modified U-Net Architecture for Automated Breast Thermogram Segmentation and Classification.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- Infrared thermography in clinical practice: a literature review.European journal of medical research · 2025Review
- Water retention influences thigh skin temperature variation post-exercise: preliminary study of bioimpedance analysis and thermography data.Frontiers in sports and active living · 2025Article
- Observational
- Early breast cancer detection in CT scans using convolutional neural bidirectional feature pyramid network.PeerJ. Computer science · 2025Article
- Thermal imaging in biomedical research: a non-invasive technology for animal models.Frontiers in veterinary science · 2025Review
- The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography.Journal of imaging · 2024Article
- Artificial intelligence-enhanced infrared thermography as a diagnostic tool for thyroid malignancy detection.Annals of medicine · 2024Article
- Review
- Infrared thermography: An investigative tool for detection of subclinical bleed in hemophilia on prophylaxis.The Indian journal of medical research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.
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Identifiers
What OpenQuestion holds
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.