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    Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance
    (2025-06-01)
    Jamrasnarodom, Jirakorn
    ;
    Rajborirug, Pharuj
    ;
    Pisespongsa, Pises
    ;
    Pasupa, Kitsuchart
    Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F<inf>1</inf>-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F<inf>1</inf>-score of 0.720 on internal test sets, and localization performance with an F<inf>1</inf>-score of 0.883 on adjusted images. External validation showed improvement but with a lower F<inf>1</inf>-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings. • RGB Color Adjustment: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models. • Model Development: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments. • Performance Evaluation: Assessed model performance using mAP and F<inf>1</inf>-scores on both internal and external validation datasets.
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    Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network
    (2024-12-01)
    Benchawattananon, Phongsachot
    ;
    Siritaratiwat, Apirat
    ;
    Supnithi, Pornchai
    ;
    Nishioka, Michi
    ;
    Perwitasari, Septi
    An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract: (Figure presented.)