ArticleMicroscopy research and technique2025
Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy.
Article in Microscopy research and technique, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Review
- Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy.Microscopy research and technique · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Histopathological tissue grading is critical for disease diagnosis and treatment, but manual grading is labor-intensive and time-consuming, requiring expert pathologists. This study presents an efficient analysis of squamous cell carcinoma (SCC) histopathological images using machine learning (ML) and deep learning (DL) models. Five ML models-support vector machine, Naïve Bayes, decision tree, k-nearest neighbor (KNN), and neural network-were trained with 5-, 7-, and 10-fold cross-validation. Discrete wavelet transform along with gray level co-occurrence matrix and histogram features extracted 360 features per image, and Student's t-test selected 114 key features. Among ML models, KNN with sevenfold cross-validation achieved 98% accuracy. Additionally, a convolutional neural network (CNN) trained achieved 98.23% accuracy in automated classification. These results suggest that combining ML for feature analysis with interpretable DL models can lead to more accurate and efficient SCC grading, reducing reliance on manual pathology.
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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.