Background: Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to identify lung cancer in its early stages using CT scan images. Methods: Pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, InceptionResNetV2, Xception, VGG-19, and InceptionV3, were used for lung cancer detection. Once the disease was identified, the tumor’s region was segmented using models such as UNet, SegNet, and InceptionUNet. Results: The InceptionResNetV2 model achieved the highest detection accuracy of 98.5%, while UNet produced the best segmentation results, with a Jaccard index of 95.3%. Conclusions: The study demonstrates the effectiveness of deep learning models, particularly InceptionResNetV2 and UNet, in both detecting and segmenting lung cancer, showing significant potential for aiding early diagnosis and treatment. Future work could focus on refining these models and exploring their application in other medical domains.

Clouds play a pivotal role in determining the weather, impacting the daily lives of everyone. The cloud type can offer insights into whether the weather will be sunny or rainy and even serve as a warning for severe and stormy conditions. Classified into ten distinct classes, clouds provide valuable information about both typical and exceptional weather patterns, whether they are short or long-term in nature. This study aims to anticipate cloud formations and classify them based on their shapes and colors, allowing for preemptive measures against potentially hazardous situations. To address this challenge, a solution is proposed using image processing and deep learning technologies to classify cloud images. Several models, including MobileNet V2, Inception V3, EfficientNetV2L, VGG-16, Xception, ConvNeXtSmall, and ResNet-152 V2, were employed for the classification computations. Among them, Xception yielded the best outcome with an impressive accuracy of 97.66%. By integrating artificial intelligence technologies that can accurately detect and classify cloud types into weather forecasting systems, significant improvements in forecast accuracy can be achieved. This research presents an innovative approach to studying clouds, harnessing the power of image processing and deep learning. The ability to classify clouds based on their visual characteristics opens new avenues for enhanced weather prediction and preparedness, ultimately contributing to the overall accuracy and reliability of weather forecasts.

Background/Objectives: The aim of this study was to classify nailfold videocapillaroscopy (NVC) images obtained from patients with systemic sclerosis (SSc) and healthy individuals using deep learning methods. Methods: Between January and June 2025, 1280 NVC images were recorded from 50 SSc and 30 healthy individuals. The images were classified by two rheumatologists as normal, early, active, and late-stage SSc patterns. After removing 191 unclassifiable and 112 low-quality images, 977 usable images remained. To ensure balanced classes, 245 normal images were excluded. The final dataset was split into training (70%), validation (20%), and test (10%) sets. Six different deep learning models (MobileNetV3Large, ResNet152V2, Xception, VGG-19, InceptionV3, and NASNetLarge) with varying levels of complexity and computational efficiency were selected to compare their performance. Accuracy, precision, recall, F1 Score, and cross-entropy loss were employed as performance metrics. These metrics are commonly used in the literature to evaluate the effectiveness of classification models. Results: Deep learning models achieved an accuracy ranging from 90.6% to 98.9%, a precision of 93.4% to 98.9%, a recall of 90.6% to 98.8%, an F1 score of 92% to 98.9%, and an ROC AUC performance between 99% and 100%. InceptionV3 demonstrated the best performance with an accuracy of 98.95%, a precision of 98.94%, a recall of 98.80%, and an F1 score of 98.88%. In terms of test loss, the lowest values were observed in the InceptionV3 and NasNetLarge models, both with a loss of 0.03. Overall, the ROC AUC values for all models ranged between 98.99% and 100%, indicating competitive performance. Conclusions: The current findings suggest that deep learning methods may be capable of classifying NVC images as accurately as experienced rheumatologists.

Mastitis is one of the most significant infectious diseases affecting dairy cattle. It has a drastic impact on the animals welfare and poses serious economic losses to dairy production. In addition to examining the milk, evaluating the mammary tissue, especially the superficial inguinal (supramammary) lymph nodes, are essential for diagnosis and prognosis. This study aims to assess the effectiveness of artificial intelligence -based deep learning models in detecting mastitis from ultrasonography images of superficial inguinal lymph nodes. The study was conducted on 252 Brown Swiss cows aged 3–6 years, which were classified into three groups according to the California Mastitis Test and clinical examination: clinical mastitis (n = 84), subclinical mastitis (n = 84) and a control group (n = 84). Six pre-trained deep learning architectures were used to process B-mode ultrasonographic images: MobileNetV3, EfficientNetV2B3, Xception, InceptionV3, NasNet, InceptionResNetV2 and ConvNeXtSmall. All models performed satisfactorily, with EfficientNetV2B3 achieving the highest accuracy (96.87 %), precision (97.53 %) and F1 score (96.79 %), with a area under the curve of (100 %). These results suggest that integrating ultrasonographic and echotextural data with an AI-based model could be a valuable resource for the early detection and accurate diagnosis of mastitis, in line with the goals of precision livestock farming.

Background/Objectives: Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. Accurate clinical identification of furcation involvement is essential for improving treatment outcomes. This study aimed to evaluate the accuracy and effectiveness of various artificial intelligence (AI) algorithms in detecting furcation defects (FD) in mandibular molars. Methods: A total of 654 panoramic radiographs were randomly selected from patients who visited the Department of Oral and Maxillofacial Radiology at the Faculty of Dentistry, Ankara University. Each image was labeled as either “healthy” or “FD” and subsequently preprocessed. The performance of different deep learning algorithms in identifying FD was subsequently evaluated. Results: In the classification models employed, the highest scores were calculated as accuracy 97.9%, precision 97.10%, sensitivity 97.08%, and F1 score 97.09% in the Xception model. In the segmentation tests, the highest scores were calculated as accuracy 99.96%, precision 99.26%, sensitivity 97.57%, and F1 score 98.41% in the ENet model. Conclusions: Results of this study indicated that the use of artificial intelligence systems in detecting furcation involvement in mandibular molar teeth in panoramic radiography images is promising. Further studies covering larger data sets, including maxillary molar teeth, will increase the success rates in detecting furcation involvement.