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Author: search.filters.author.Thanasitthichai, SomchaiSubject: search.filters.subject.breast cancer

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    Item type:Publication,
    Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection
    (2023-01-01)
    Intasam, Anongnat
    ;
    Promworn, Yuttachon
    ;
    Juhong, Aniwat
    ;
    Thanasitthichai, Somchai
    ;
    Khwayotha, Sirihattaya
    This research aims to find the best use optimizer for the task while reducing training time. We optimized the YOLOv5s model and focused on three optimizers, including the Stochastic Gradient Descent (SGD) optimizer, Adaptive Moment Estimation (Adam) optimizer, and Adam with Weight Decay Regularization (AdamW) optimizer. This research utilized 1,471 mammogram images from National Cancer Institute and Udonthani Cancer Hospital, Thailand. A dataset of mammograms was labeled into six classes, including Masses Benign, Masses Malignant, Calcifications Benign, Calcifications Malignant, Associated Features Benign, and Associated Features Malignant, to classify the results accurately. We found that the SGD optimizer outperformed the others, with a mean average precision (mAP) of 0.91, a precision of 0.92, a recall of 0.85, and the shortest training time of about 5.453 hr.
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    A comparative study of convolutional neural networks for mammogram diagnosis
    (2022-01-01)
    Intasam, Anongnat
    ;
    Promworn, Yuttachon
    ;
    Thanasitthichai, Somchai
    ;
    Piyawattanametha, Wibool
    This work evaluates and compares the architectures: Inceptionv4, InceptionResnetV2, and Resnet152, to classify benign and malignant. We evaluate the architectures with a statistical analysis base on the received operational characteristics (ROC), accuracy, precision, recall, and F1 score. We generate the best results with the CNN InceptionResnetV2 trained with two classes on a balanced mammogram database. The results for benign cases have a ROC of 0.93, a precision of 0.8319, a recall of 0.9216, and an F1-score of 0.8744. The results for malignant cases have a ROC of 0.91, a precision of 0.9121, a recall of 0.8137, and an F1-score of 0.8601.