Robust Deep Learning Approach for Automating the Epithelial Dysplasia Detection in Histopathology Images
DOI:
https://doi.org/10.56294/dm2025679Keywords:
Evolutionary Optimization, Metaheuristic, engineering design, LeadershipAbstract
Automated image analysis using deep learning techniques helped diagnose epithelial dysplasia in normal tissues. This study examined a hybrid approach that combined traditional image processing methods with deep learning for accurate tissue classification. A diverse, annotated dataset of epithelial dysplasia histology images was created and processed. To mitigate overfitting, a pre-trained convolutional neural network (CNN) model was finetuned with optimized hyperparameters. Performance metrics, including accuracy and precision, were assessed using an independent test dataset. The Structural Similarity Index (SSIM) was applied to enhance image contrast. The optimized deep learning model outperformed conventional methods in diagnostic accuracy. The hybrid approach demonstrated significant effectiveness in distinguishing epithelial dysplasia in medical images. The results highlighted the potential of integrating deep learning algorithms with traditional image processing techniques for automated medical diagnostics. This method showed promise for future applications in enhancing diagnostic accuracy and efficiency.
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Copyright (c) 2025 Jamal Zraqou, Riyad Alrousan, Najem Sirhan, Hussam Fakhouri, Khalil Omar, Jawad Alkhateeb (Author)
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