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    Mammogram Analysis with YOLO Models on an Affordable Embedded System
    (2026-01-01)
    Intasam, Anongnat
    ;
    Piyawattanametha, Nicholas
    ;
    Promworn, Yuttachon
    ;
    Jiranantanakorn, Titipon
    ;
    Thawornwanchai, Soonthorn
    Background/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 ± 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications.
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    Advancing Breast Cancer Identification: Exploring Deep Learning Models for Improved Detection
    (2025-01-01)
    Samrankit, Thitiphon
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    Surakiat, Kanyanut
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    Tamang, Sudarshan
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    Sharma, Aayushma
    ;
    Paing, May Phu
    (1) Background: Breast cancer is the uncontrolled growth of abnormal cells within the breast tissue, and it can be either malignant or benign. Malignant tumors are cancerous and have the potential to invade and spread to other parts of the body while benign tumors are non-cancerous. Traditional breast cancer screening methods have limitations from artifacts and positioning errors. (2) Methods: This study employs various deep learning models—GELAN-C (Generalized Efficient Layer Aggregation Network – Compact version), YOLOv8 (You Only Look Once – version 8), RTMDet (Real-Time Models for Object Detection), DETR (DEtection TRansformer), YOLO-NAS (You Only Look Once – Neural Architecture Search), and Detectron2—to identify the most effective and adaptable model for breast cancer diagnosis. (3) Results: From our experiment, GELAN- C outperformed other models providing exceptional accuracy with the highest mean average precision (mAP) scores of both thresholds. Specifically, GELAN-C achieved a mAP@0.50 of 0.983, which reflects overall object detection accuracy. Additionally, it maintained a strong mAP@0.50-0.95 of 0.868 indicating its robustness and precision across varying levels of detection difficulty.
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    Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection
    (2023-01-01)
    Intasam, Anongnat
    ;
    Promworn, Yuttachon
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    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.