# Object Detection for Brain Cancer Detection and Localization
## 4 Related Work
Moreover, differently from the proposed method, our dataset is composed of a decidedly smaller number of images; in fact, typically in the medical field, images that are accurately labeled by medical personnel are not often available. [PERSON] and colleagues [28] considered the YOLOv5 model for the detection of the following brain cancers: Meningioma, Pituitary, and Glioma. They exploited 720 images for model training and 180 images for validation. The following performances were obtained: with regard to the Meningiom detection, an accuracy of 0.129 was obtained; relating to the Piuitary detection, an accuracy of 0.165 was reached; and for the Glioma identification, the accuracy was 0.140. The [EMAIL_ADDRESS] metric was 0.145, while the [EMAIL_ADDRESS] was equal to 0.08, thus demonstrating that the task to detect brain cancer is hard. In any case, we obtain better performances with respect to the authors in [28]; in fact, while the authors obtained values for the [EMAIL_ADDRESS] metric of 0.145 and for the [EMAIL_ADDRESS] equal to 0.08,the YOLOv8 model we adopted obtained values equal to 0.935 and 0.388, respectively, in the validation step. [PERSON] and colleagues [29] propose a method designed to detect whether a brain image is related to cancer. Furthermore, they experimented with the application of different segmentation algorithms (for instance, multilevel thresholding and OTSU thresholding). Differently from the proposed method, we propose the adoption of a model designed to perform the classification and the segmentation in one step, while authors in [29] consider a neural network to perform the image classification and a set of algorithms to perform the segmentation (i.e., the segmentation is performed without the adoption of deep learning; as a matter of fact, in the paper, the authors do not present the [EMAIL_ADDRESS] and the [EMAIL_ADDRESS] metrics, considering that the main aim of their method is the brain cancer image classification). [PERSON] and colleagues [30] propose a hybrid deep learning convolutional neural network long short-term memory model for classifying and predicting brain tumors. Differently from the proposed method, the authors in [30] propose a method designed to classify an image as cancerous or healthy, i.e., they do not aim to detect the cancerous area on the image (whether the presence of cancer is detected); in fact, also in this paper, the authors do not consider the [EMAIL_ADDRESS] and the [EMAIL_ADDRESS] metrics. [PERSON] and colleagues [31] propose several models to detect brain cancer from medical images. They obtain the following accuracies: 0.86, 0.82, and 0.80 when the K-nearest neighbors, the random forest, and the support vector machine are considered, respectively. In contrast, the proposed method is able to reach a precision of 0.943 and a recall of 0.932 in brain cancer detection, and, differently from the authors in [31], we also consider automatic cancer localization. Authors in [32] propose the adoption of the YOLOv7 model for gastric cancer detection through the integration of a squeeze and excitation attention block. They obtain precision, recall, F1-score, and mean average precision values of 0.72, 0.69, 0.71, and 0.71, respectively. By employing the modified YOLOv7 model, the authors state that endoscopists can benefit from real-time lesion detection and identification, leading to improved analysis of endoscopic images, facilitating early diagnosis, and diminishing the need for extensive operator expertise. Differently from authors in [32], the proposed paper is focused on brain cancer detection and localization, and we obtain following performances: a precision equal to 0.943, a recall of 0.932, and an mAP_0.5 of 0.941. [PERSON] and colleagues [33] propose the adoption of a Mask Region Based Convolutional Neural Networks (RCNN) to detect and localize brain cancer.