# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 2 Materials and Methods
### Preprocessing Image Data
* Drop-out: A technique randomly drops out units in a neural network layer during training to prevent overfitting. This technique can decrease the co-adaptation of features and increase the diversity of feature representations. * Affine transform: A technique that applies geometric transformations such as scaling, rotation, translation, and shearing to the images. This technique can increase the invariance of the model to geometric variations and improve its performance on unseen images. Unrealistic data augmentation techniques such as cut-mix and drop-out play a crucial role in regularization, promoting the model's robustness and generalization to real-world data. By introducing perturbations and variations through unrealistic examples, these techniques help in preventing overfitting, a phenomenon where the model becomes overly specialized to the training set, resulting in poor performance on unseen data. Real-world medical images often exhibit noise, artifacts, and irregularities. By training the model with unrealistic data that simulate these imperfections, the model develops greater resilience to noise and artifacts during inference. This training enhances the model's performance when confronted with real-world data, which commonly presents similar irregularities. Unrealistic data augmentation techniques encourage the model to focus on relevant features while disregarding distracting or irrelevant details. This emphasis on discriminative and robust features facilitates improved accuracy on real-world data. Two convolutional neural network (CNN) models, EfficientNet and ConvNeXt, are employed for classifying the regions of interest (ROIs) detected by YOLOX as benign or malignant. EfficientNet adjusts the network depth, width, and resolution using a compound coefficient, while ConvNeXt utilizes grouped convolutions with cardinalityas a hyperparameter that controls the number of convolution groups. Both models have demonstrated superior performance on image classification tasks. Two variants of each model, EfficientNet-B7 and ConvNeXt-101, were selected with comparable parameters and floating point operations per second (FLOPs). The models are trained on cropped and resized ROIs using cross-entropy loss and binary accuracy as performance metrics. Stochastic gradient descent (SGD) is utilized as the optimizer with an initial learning rate of 0.01 and step decay scheduler. Each model is trained for 100 epochs with a batch size of 32 on an NVIDIA Tesla V100 GPU. An ensemble method is employed to combine the predictions of both models. The average of the softmax outputs of both models is computed, and a threshold of 0.5 is utilized to obtain the final binary prediction. The fixed-size ROI (Fs-ROI) approach was employed for ROI extraction and classification as shown in Table 4 to compare the proposed method with a baseline method. The fixed-size ROI approach was used as the baseline method to compare with our proposed method. This approach involves centering a \(224\times 224\) pixel bounding box on each lesion on the basis of lesion location annotations from the mammography datasets. The extracted ROI images are then classified into cancer or noncancer classes using the same deep-learning models (EfficientNet and ConvNeXt) and data augmentation techniques (mix up, cut-mix,  
Figure 3: Example of data augmentation for increasing the diversity and robustness of the dataset. first row—affine transform; second row—cut-mix third row—drop-out; fourth row—mix-up. drop-out, and affine transform) as our proposed method. However, the fixed-size bounding box has several limitations. Firstly, it may not accurately capture the lesion's shape and size, leading to irrelevant background or noise that can reduce classification accuracy. Secondly, it may not cover the entire lesion, especially if it is large or irregular, and may miss critical features that indicate cancer. Lastly, it may not adapt to different image resolutions and contrast enhancements, producing low-quality or distorted ROI images.