## 1 Introduction  
Antimicrobial susceptibility testing (AST), conducted in a laboratory setting or under artificial conditions, aims to determine the susceptibility of bacteria from samples taken from a living organism to specific drugs and their concentrations during an infection. The agar dilution, broth microdilution, and disk diffusion methods are currently widely used in antimicrobial susceptibility tests. The disk diffusion method (DDT) is preferred in microbiology laboratories for its simplicity, repeatability, and cost-effectiveness in determining antibiotic sensitivity [1]. In the DDT method, bacteria at a specific concentration are spread on a culture medium, and disks soaked in antibiotics are placed on top. An inhibition zone (a circular area where bacteria do not proliferate) is formed around the disk. Judgments regarding the sensitivity of the microorganism are made by measuring the inhibition areas formed around the disk [1; 2; 3]. The measurement of zone diameters is conducted using a caliper or ruler. However, this method poses a risk of contamination for the person performing the measurement and may lead to user-related errors. At present, antimicrobial susceptibility testing through image processing is performed in many laboratories, using biomedical devices or manual measurement. The images formed after incubation are analyzed 20-24 h after the culture is placed in the prepared setup [4]. In the field of microbiology, image processing is mostly used to modify, analyze, or improve existing images. In the reviewed studies, the detection of antibiotic disks with image processing methods was mostly performed using the OPENCV-Hough Circle Transform method. After determining the position of the antibiotics in the inhibition zone measurements, the images were subjected to pixel processing by passing them through various filters. The determination of antibiotic types was achieved through various text identification and model comparison methods [5]. While manual examinations by physicians have yielded antibiogram analyses, automated antibiogram image processing has not yet achieved routine clinical use. Further research is required to develop image processing methods associated with artificial intelligence for a combined approach utilizing both manual and automated antibiogram analyses. Our study aimed to evaluate antibiograms through implementing an efficient image processing algorithm, using a machine learning method to determine the growth inhibition zones from plate images, and identifying the types of antibiograms utilizing convolutional neural networks (CNNs), a deep learning technique. With this methodology, it is possible to precisely determine the diameters of the inhibition zones and antibiograms using image processing and classification libraries. In this study, we minimize contamination problems through performing automatic diameter measurement according to a reference object, guaranteeing a faster and more efficient measurement process through automation of the diameter measurements. Then, we use a machine learning algorithm to determine the types of antibiograms. Thus, we aim to bring an innovative perspective for the identification of antibiotic disks, which are coded with letters or numbers to provide new data with the help of artificial intelligence. The AST is a clinical test that detects the susceptibility of a microorganism to antibiotics. The examination of antibiogram tests is usually undertaken manually, which can lead to human error and lengthy procedures. In order to read and explicate the antibiograms, some effort has been put into the development of automated devices; however, it is often costly to operate these medical devices in different locations. Therefore, automated computerized image processing has been identified as an effective method for the detection and interpretation of antibiograms.