**Require:** Input image *I*

**Ensure:** Bounding boxes *B* and class probabilities *C*


Note that this algorithm assumes that the YOLOv4 architecture has already been trained on a large dataset of images with labeled objects and that the resulting model has been saved and can be loaded for inferences on new images. The backbone network, neck network, and detection head are all components of the YOLOv4 architecture, and their specific details are beyond the scope of this pseudo-coded algorithm [26].

One way that YOLOv4 has been used in medical image analysis has been in the detection of abnormalities and lesions in images [27]. For example, it was used to identify abnormalities in CT scans of the brain, which could then be used to diagnose and treat brain tumors. By analyzing CT scans with YOLOv4, healthcare professionals could more accurately identify abnormalities and determine the appropriate course of treatment.

During the COVID-19 pandemic, YOLOv4 has also been used to analyze chest X-rays, which have often been used to diagnose the virus [28]. By detecting characteristic patterns associated with COVID-19, such as lung abnormalities, YOLOv4 assisted healthcare professionals in making accurate diagnoses and providing timely treatments for patients [29]. In addition to its use in detecting abnormalities within images, YOLOv4 has also been used to detect objects in images, such as medical instruments and organs. This was particularly useful for identifying and tracking objects during surgical procedures, such as in the detection of brain tumors [30].

### **4. Medical Data Classification and Detection**

Medical data classification and image detection are two critical areas in healthcare that could benefit from the latest advancements in machine-learning and computer-vision technologies. In recent years, there has been a significant increase in the amount of medical data generated due to the availability of electronic health records and medical imaging technologies. This growth in medical data has provided new opportunities for developing more accurate and efficient methods for classification and image detection, which could lead to improved diagnoses, treatments, and patient outcomes.
