# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
### Current State-of-the-Art in BCD
However, they noted that DenseNet's complexity and the extensive use of dense connection blocks led to model overfitting, while MobileNet, being more lightweight, overlooked multilevel features. In a related study, [PERSON] et al. [20] introduced an enhanced version of the YOLOv3 network, termed an anchor-free-YOLOv3, specifically for mass detection in mammograms. They addressed common issues in bounding box regression by incorporating Generalized Intersection over Union (GIoU) loss instead of the traditional Mean Squared Error (MSE). To further refine object detection, they utilized focal loss, which counters the negative impact of a large number of easy negatives. Their feature fusion technique, the summation method, was integrated into the model's top-down pathway. [PERSON] et al. [21] focused on evaluating the You Only Look Once version 5 (YOLOv5) for detecting and classifying breast lesions in dynamic contrast-enhanced MRI (DCE-MRI). Four YOLOv5 sub-models were tested on a dataset comprising over 2000 images, each of benign and malignant lesions. The study measured the precision, recall rate, and mean average precision to assess model performance. Among the sub-models, YOLOv5s outperformed others, with the highest precision (0.916) and mean average precision (0.894 for lesion detection. The study suggests YOLOv5s's potential for clinical applications in the rapid and accurate diagnosis of breast lesions on DCE-MRI, indicating its value in AI-assisted imaging diagnosis. Complementing these studies, [PERSON] et al. [22] developed the YOLO-LOGO model, combining the strengths of YOLO and LOGO (local-global) networks for mass detection and segmentation in mammograms. The process begins with the YOLOV5 model identifying mass locations, followed by cropping these masses from the images. The LOGO training approach then separately trains global and local transformer branches on both full and cropped images. The final segmentation decision is derived from merging these two branches. This innovative approach was validated using both the CBIS-DDSM and INbreast datasets, demonstrating enhanced balance in model performance for both training and segmentation tasks. [PERSON] et al. [32] focused on a deep transfer learning-based model, assessing various pre-trained models for BC classification. Their comparative study using the MIAS benchmark demonstrated that the VGG-16 architecture, based on transfer learning, surpassed other models like Inception V3, ResNet50, and Inception-V2 ResNet in performance. However, they observed that the VGG-16 model did not account for multiscale features, which are crucial for comprehensive lesion detection and classification. [PERSON] et al. [33] introduced a two-stage deep-learning framework utilizing Faster-RCNN. Their model first extracted the Region of Interest (RoI) and then classified breast masses into different categories. Tested against the INBreast benchmark, their framework showed superior performance compared to baseline architectures, indicating effectiveness in both lesion detection and classification. Table 1 provides a critical analysis and brief description of various BCD frameworks, highlighting their methodologies and areas for improvement.