# Object Detection for Brain Cancer Detection and Localization
## 4 Related Work  
In this section, we provide an overview of the current state-of-the-art methods in the field of brain cancer detection using machine learning techniques. [PERSON] and colleagues [19] have presented a method aimed at discriminating between benign and malignant brain tumors by analyzing magnetic resonance images (MRI). Their proposed approach has achieved an impressive accuracy of approximately 95%. Figure 7: The sixteen images once processed by the proposed model: it is possible to note the red bounding boxes, automatically added by the proposed model, related to the cancerous area. Moreover, for each bounding box, there is the detail about the prediction percentage. In their study, [PERSON] et al. [20] employed a neural network algorithm to classify medical images as either benign or malignant tumors. However, their implementation of the canny edge detection algorithm resulted in an inaccuracy rate of approximately 15-16%. Authors in [21] investigated the utilization of the multi-layer perceptron (MLP) and naive Bayes classification algorithms to differentiate between malignant and benign brain tumors. They focused on analyzing texture features extracted from the medical images to achieve accurate tumor classification. [PERSON] et al. [22] conducted a study where they explored statistical texture features extracted from both normal and malignant medical images. They employed the nearest neighbors classifier as the classification algorithm for their analysis. Their study reported a classification rate of 80% for distinguishing between normal and malignant medical images using these texture features. [PERSON] et al. [23] proposed a method that involves extracting features based on texture, symmetry, and intensity from brain medical images. They employed the AdaBoost algorithm to construct a model for classifying the MR images as either normal or abnormal. Their approach achieved an impressive accuracy of 96.82% in this classification task. [PERSON] and colleagues [24] investigated the use of texture features along with neural network and nearest neighbors classifiers for classifying brain medical images as either normal or abnormal. Their study reported an accuracy of 70% when employing the nearest neighbors classifier, and an accuracy of 72.5% when using the neural network classifier for this classification task. A hybrid approach combining a genetic algorithm and support vector machine (SVM) was proposed in [25] for the classification of brain medical images. The features utilized in this approach encompassed statistical, wavelet, and frequency transformations. The average accuracy achieved by this hybrid method was reported to be 83.22%, with the accuracy range varying between 79% and 87%. In a study conducted by researchers in [26], the effectiveness of neural networks in detecting brain cancer in MRI images was investigated. The study focused on three types of brain cancer: Acoustic glioma, Optic glioma, and Astrocytoma. They achieved an average recognition rate of 78% using neural networks. The dataset used for the study consisted of a total of 30 MRI images. [PERSON] and colleagues [27] proposed the YOLOv8 model as a generalized model for real-time detection of flying objects that can be used for transfer learning. They achieve this by training a first generalized model on a dataset containing 40 different classes of flying objects, by extracting abstract feature representations. The first model obtains a mAP50-95 of 0.685 and the refined one obtains an improvement of the mAP50-95 metric equal to 0.835 when two datasets are considered: the first one of 11,998 images and the second one of 15,064 images, for a total of 27,062 images. The results obtained by the authors are extremely interesting; unlike them, we propose the detection of brain cancer cells, which from a graphical point of view are objects not often different from the background of the image and therefore difficult to identify.