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    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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    Investigation of deep learning optimizer for water pipe leaking detection
    (2019-07-01)
    Arunsuriyasak, Peerachai
    ;
    Boonme, Phattraporn
    ;
    Phasukkit, Pattarapong
    Nowadays, Deep learning plays an important role in complex problems. Thus, one of important algorithm part is an optimizer. This paper aims to improve algorithm using optimizers. Adam optimizer, a powerful and effective optimizer, was used to adjust parameters in Deep Neural Networks model. Which, object datasets consist leaking water pipe, non-leaking water pipe are used to classify 2 object labels. Nevertheless, RMSprop and Adadelta are alternative optimizers that can be used in Deep Neural Network. Other than that, this experiment has been shown Adam gave an accuracy at 98.973% for leaking water pipe and 97.466% for non-leaking water pipe. While, Adadelta gave 76.755% and 70.448%. And RMSprop gave 98.973% and 97.466%.