# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Results  
A 5-fold cross validation was applied on every experiment to ensure that training and validation partitions were independent of each other and that the training was not biased. To this end, the dataset, which was randomly reordered, was split into five partitions. Since the dataset is composed of 50 images (see Section 3.1 for more details), each partition is formed by 10 images. Thus, each deep neural network model used one split for validation (10 images) and the remaining partitions for training (40 images). The model used in this study was the YOLOv8x neural network, which consists of a structure of 640 pixels and 68.2 million parameters [28]. The YOLO architecture is based on a feature pyramid neural network consisting of multiple layers that detect objects at different scales. This pyramid consists of two main parts: the head, responsible for launching the predictions, and the backbone, which improves the information flow between the different layers of the network. Both parts use a module called C2f (cross-stage partial bottleneck with two convolutions), which merges contextual information with high-level features to enhance detection performance [30; 31]. The model was trained using pre-trained weights from common object detection. As previously mentioned in Section 2, the use of this model is not limited to the proposed approach and can be modified for use with other object detection models if desired. To avoid a high number of false positives, only predictions with confidence and intersection over union values of at least 0.7 were selected (i.e., \(\upsilon_{min}=0.7\)). Regarding the augmentation algorithms used in the training processing, the YOLO algorithm automatically applies multiple data augmentation techniques by default. The algorithms included hue adjustments, modification of saturation and brightness values, image rotations, vertical and horizontal image flips, image scaling, image shearing from different angles, perspective transformation, vertical and horizontal image translation, channel shifting (from RGB to BGR), the compositing of several imagesinto mosaic, the deletion of parts of the image, and image fraction crop. The remaining parameters of YOLO were kept at default values. Table 1 exhibits more detail about the parameters and the tuned values. As mentioned in Section 2, the use of the YOLOv8x detector is not confined to the proposed methodology and can be substituted with other detector models if required. In this work, the YOLOv8x neural network was chosen after a preliminary performance comparison between different YOLOv8 versions, such as YOLOv8n or YOLOv8m. Other detectors, such as Faster-RCNN ResNet or MobileNet models, were discarded because their detection performances, in general, are similar to those yielded by YOLOv8 models, and their computational times are considerably longer [32]. The considered initial models (YOLOv8 versions) were trained over the raw image dataset, and their achieved performances related to the accurate detection of dicentric chromosomes were analyzed. Since dicentric chromosome detection involves evaluating medical images, the computational execution time is not crucial in terms of real time. Therefore, this measure was omitted in our comparison. As expected, YOLOv8x achieved the highest scores in that preliminary comparison. Thus, YOLOv8x was selected as the architecture detector to compose the ensemble proposed in our methodology. The possible combinations of the thresholding technique used in binarization (Spline or Otsu), the morphological filter type (opening or closing), and the filter size (2 \(\times\) 2, 3 \(\times\) 3, 4 \(\times\) 4 and 5 \(\times\) 5) constituted the configurations with which each model was trained. Regarding the consensus function, it combines the prediction provided by the different methods that compose the ensemble. In this work, that combination was considered to be the merging of the detections with an iou value higher than 0.50.