ArticleCurrent medical imaging2025
Enhancing Medical Image Classification through Transfer Learning and CLAHE Optimization.
Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- An automated tuberculosis detection approach using deep learning and machine learning techniques from chest X-ray images: a step towards effective diagnosis.Polish journal of radiology · 2026Article
- Dermoscopically informed deep learning model for classification of actinic keratosis and cutaneous squamous cell carcinoma.Scientific reports · 2025Article
- Optimized Lung Nodule Classification Using CLAHE-Enhanced CT Imaging and Swin Transformer-Based Deep Feature Extraction.Journal of imaging · 2025Article
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Authors and funding
2 authors.
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Abstract
introductionThis paper examines the impact of transfer learning and CLAHE (Contrast Limited Adaptive Histogram Equalization) optimization on the classification of medical images, specifically brain images.
methodsFour different setups were tested: normal images without data augmentation, normal images with data augmentation, CLAHE-processed images without data augmentation, and CLAHE-processed images with data augmentation.
resultsThe results show that using CLAHE combined with data augmentation significantly improves classification accuracy. Specifically, the combination of CLAHE and data augmentation achieved a precision of 0.90, a recall of 0.87, an F1-score of 0.89, and an accuracy of 0.86, outperforming the other setups.
conclusionThese findings highlight the effectiveness of CLAHE optimization in the context of transfer learning, particularly when data augmentation techniques are also applied, leading to an overall improvement in the performance of brain image classification models.
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