# Object Detection for Brain Cancer Detection and Localization
## 2 The Method  
In this section, we describe the proposed method for automatic detection and localization of brain cancer by analyzing brain MRIs. The focus of this paper is specifically on brain MRIs, but the proposed method can be readily applied to other types of bioimages, such as those related to the lung or other organs. Figure 1 shows the workflow of the proposed method, designed to detect and localize brain cancer. In order to build an effective deep learning model for the detection and localization of brain cancer from MRI scans, it is essential to have a dataset that consists of brain cancer MRIs along with corresponding annotations (i.e., bounding boxes) indicating the localization of the cancerous regions. We highlight the importance of having a high-quality dataset that includes both the MRIs and precise annotations of the cancer localization. Such a dataset serves as the foundation for training the object detection model effectively. By having accurate annotations, the model can learn to identify and locate the specific regions within the brain MRI scans that indicate the presence of cancer. Having a reliable and well-annotated dataset is crucial in the development of a robust and accurate object detection model for brain cancer detection and localization. It ensures that the model is trained on representative and informative data, enabling it to make precise predictions when applied to new, unseen MRI scans. In Figure 2, we show an example of an MRI related to brain cancer. Within the MRI shown in Figure 2, it is possible to observe a lighter gray, almost white area on the left side of the image, indicating where the brain tumor is located. Figure 1: The workflow of the proposed method for brain cancer detection and localization. Figure 2: An example of brain MRI. In order to construct a model that is both efficient and capable of accurately predicting unseen images, a diverse dataset was compiled. This dataset comprises images captured from various angles, under different conditions, and featuring different types of tumors. Each image within the dataset has a unique size. However, to facilitate further analysis, a preprocessing step is required to resize all the images to a consistent dimension. Once the images are obtained, we need to define the class for the detection of the bounding box for the brain MRI: in this case, we have one class that is tumor, related to the brain cancer presence. We annotated each image by drawing bounding boxes around each detected object. This annotation process was carried out using the Labelbox web application ([[https://labelbox.com/](https://labelbox.com/)]([https://labelbox.com/](https://labelbox.com/)), accessed on 8 August 2023), i.e., a platform designed to perform data annotation tasks. The subsequent step involves image augmentation, which refers to a collection of techniques that expand the existing dataset without the need for gathering new samples. Data augmentation involves applying controlled random modifications to existing images and generating modified versions of them. This technique is commonly employed in training artificial neural networks, as it enables them to \"learn\" more effectively and accurately as the size of the training dataset grows. Specifically, we employ data augmentation techniques to generate modified images of brain MRIs with controlled random variations, such as rotations, flips, cuts, and trims. The objective behind applying data augmentation in this context is to make the model capable of effectively detecting tumors regardless of their position within the image. Additionally, augmented data are utilized to address the issue of overfitting, which occurs when a statistical model becomes too specialized in fitting the observed data sample due to an excessive number of parameters compared to the number of observations. By introducing variations through data augmentation, the structured neural network can learn to recognize recurring patterns from the augmented data, rather than simply memorizing specific examples.