The CNN Model with YOLO Architecture for Ultrasonography Images in Early Breast Cancer Detection - Ayuningtyas, Astika; Wintolo, Hero; Sumari, Arwin Datumaya Wahyudi; Setyaningsih, Emy; Pujiastuti, Asih; Honggowibowo, Anton Setiawan; Nuryatno, Edi Triono; Kusumaningrum, Anggraini (Bright Publisher, 2025)
The rapid development of object detection technology has opened new opportunities in the healthcare sector, especially in early cancer detection. This paper presents a deep learning-based breast cancer detection system using ultrasound images. The primary goal of this study is to create a model that can effectively differentiate between malignant and benign breast tumors, assisting in early diagnosis. The proposed system employs the Convolutional Neural Network (CNN) algorithm with You Only Look Once version 5 (YOLOv5) architecture, which is renowned for its high speed and accuracy in object detection tasks. A dataset comprising 10,954 ultrasound images was used to train the model, with 70% allocated for training, 20% for validation, and 10% for testing. The study reveals that the model achieves a high accuracy rate of 92.8% for malignant tumor detection and 99.1% for benign tumors, with precision rates of 99.6% for malignant tumors and 97.5% for benign tumors. These results demonstrate the feasibility of the proposed model as a reliable tool for early breast cancer detection. The findings highlight the potential of deep learning in medical image processing, suggesting that this technology could be further developed into an accessible, efficient early detection system for breast cancer in clinical settings. Future research could explore the integration of additional imaging modalities and the application of this model in real-world healthcare environments