## 5 Conclusions  
In test applications of antibiograms, human error can occur when measuring the antibiotic inhibition zone. Despite this, manual measurement methods are still in use. This study aimed to enhance the efficacy of antibiogram susceptibility test measurements through minimizing human factor errors occurring during these measurements. In this study, inhibition zone measurements of the antibiogram susceptibility test were performed by means of automatic identification methods and an artificial intelligence-based clustering model. The deep learning CNN model was then used to classify the antibiotic types. The first of the three steps of our study involved determining the threshold value within a certain coefficient range used for identifying antibiotics, and successfully detecting the antibiotic disk image using the \"Hough Circle\" method, due to its high contrast value relative to the Petri dish. The antibiotic disks in six test Petri dishes were fully detected. In this step, the success of our method was 100%. [PERSON] et al. utilized antibiotic disk diameters of 6 mm in their studies and indicated that this corresponded to 55 pixels using pixel measurement techniques. In their research, they reported detecting antibiotics by passing them through different filters, including a blurring filter [11]. In step 2, despite utilizing various noise removal methods in the zone measurement, some zones were not accurately detected. [PERSON] et al. discussed the challenges of detecting zones in digital images with damaged plates and extremely poor contrast between bacteria and the inhibition zone [16]. They developed and trained two machine learning models for identifying resistance mechanisms in images. While the accuracy results were promising, they were concerned about overfitting due to the small size of their training datasets, especially for the mobile application's requirements. However, the integrated Expert System managed these situations by prompting the user to confirm or exclude the presence of the shapes when they were likely to occur. Their application's fully automated measurement procedure demonstrates a 90% agreement in susceptibility categorization compared to a hospital-standard automatic system. [PERSON] et al. described the preliminary steps of image processing through a closed setup in zone measurements and suggested that problems encountered in image preprocessing can be significantly reduced by utilizing the homogeneous illumination technique [11]. Additionally, they found that image quality improved without glare and reflection when the light intensity was homogeneously distributed in a closed environment using plexiglass and vinyl material. In our study, \begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **f1-Score** & **Support** \\ \hline Accuracy & & & 0.89 & 1430 \\ \hline Macro avg. & 0.88 & 0.89 & 0.89 & 1430 \\ \hline Weighted avg. & 0.90 & 0.89 & 0.89 & 1430 \\ \hline \end{tabular}
\end{table} 
Table 4: Success rate of DenseNet201. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **f1-Score** & **Support** \\ \hline Accuracy & & & 0.91 & 1430 \\ \hline Macro avg. & 0.90 & 0.90 & 0.90 & 1430 \\ \hline Weighted avg. & 0.92 & 0.91 & 0.91 & 1430 \\ \hline \end{tabular}
\end{table} 
Table 5: Success rate of VGG16. the success of the diameter calculation of inhibition zones was measured at 87.6%. Better results can be achieved through adopting a combined method with different parameters, such as lighting, different coloring of the Petri dish background, and image processing filters with different parameters when measuring inhibition zones. Future studies should consider that different combined methods could contribute to the literature. The final phase of our study was conducted using the CNN machine learning technique. The \"amman_atb_data\" dataset, consisting of 9770 images and 23 classes, was utilized. With this dataset, a 92% antibiotic disk classification success was achieved.