ArticleJournal of imaging2020
Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images.
Article in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Assessment of the Degree of Erythema Reduction in Rosacea After Polychromatic Light Treatments.Journal of clinical medicine · 2025Article
- Quantitative Image Analysis of Vascular Skin Response to Intense Pulsed Light Therapy.Clinical, cosmetic and investigational dermatology · 2025Article
- Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks.Frontiers in medicine · 2025Article
- An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks.Diagnostics (Basel, Switzerland) · 2024Article
- DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.Scientific reports · 2024Article
- VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.Journal of imaging · 2023Article
- Computerised prediction of healing for venous leg ulcers.Scientific reports · 2022Article
- Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients.Physical and engineering sciences in medicine · 2021Article
- Deep Learning in Medical Image Analysis.Journal of imaging · 2021Article
- Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information-Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.
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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.