ArticleScientific reports2026
Lightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Fusion of genomic and pathological data for breast cancer detection using BCDNN.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a worldwide health crisis that affects a large number of women. Early detection of this disease is critical for determining effective treatment options and improving the chances of positive patient outcomes. In our study, we introduce a new method for detecting breast cancer in its earliest stages using thermographic images and a compact model that can be easily implemented on smartphones. This method is especially useful in areas where medical resources are scarce. We used multiple edge detection techniques like Canny, Roberts and Sobel, and evaluated their effectiveness to improve the accuracy of our model. Our model, which combines MobileNet V2 with a spatial attention mechanism, outperformed other deep learning networks like Inception ResNet and DenseNet121. Furthermore, with an accuracy rate of 98.88%, our proposed model outperformed current state-of-the-art algorithms. These findings point to the potential of our approach for early breast cancer detection and its practical application in resource-limited settings.
Indexed as
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.