Continuous advancements in deep learning are affecting various research areas, especially research on applications in the medical sector. A computer-aided diagnosis system that utilizes deep learning is used for classifying and detecting brain tumors in magnetic resonance imaging. Regarding brain tumors, the main diagnostic indicators are patient symptoms and outcomes of magnetic resonance imaging. Frequent changes in the symptoms of these tumors have raised serious concerns about potential misdiagnoses. Implementing computer-aided diagnosis systems can support diagnostic methods that rely on the visual assessments of physicians, potentially reducing misdiagnosis rates. In this study, we propose an enhanced computer-aided diagnosis algorithm that is optimized for brain tumor classification. We removed noise from the magnetic resonance imaging results by applying Gaussian filters, and we employed GridMask to improve the generalization performances of the deep learning models. Then, we applied Patterned-GridMask, which is a method we proposed to reduce the issue of brain tumors being obscured by standard GridMask. Under the application of Patterned-GridMask, a performance improvement of up to 6% was demonstrated across the four deep learning models used in the experiments: ViT-B/16, MaxViT-B, TresNet-M, and EfficientNetV2-M, with the highest performance being represented by an accuracy and F1-score of 97.74% and 97.75%, respectively. Using the proposed computer-aided diagnosis system, improved diagnosis results can be obtained, thereby resulting in more accurate rates of early detection, better patient outcomes, and more appropriate treatment selection.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

Gastric cancer is high-risk cancer in terms of both incidence and mortality. However, if it is diagnosed early, there is a high chance of survival. Therefore, an early diagnosis of gastric cancer and precancerous lesions is very important. Gastroscopy is one of the best methods for diagnosing gastric cancer and precancerous lesions, but it relies on visual observation by medical specialists. Accordingly, factors such as the experience or fatigue of specialists can influence diagnosis results. To alleviate these problems, we propose a computer-aided diagnosis system that can improve the efficiency of diagnosis and reduce misdiagnoses by providing a second opinion. We aimed to classify healthy tissue, gastric lesions, and early gastric cancer using a Transformer-based deep-learning classification model called Vision Transformer, which has achieved the best performance in transfer learning. We also proposed a Multi-Filter AutoAugment (MFAA) method, which increases the classification performance of the model given small amounts of medical data. The medical data augmented using MFAA are better for training deep-learning models than conventionally augmented data; we effectively enhanced the classification performance of the model using MFAA. In experiments, the model achieved an F1-score of 0.87 and area under the curve of 0.94 in the classification of abnormalities (gastric lesions including early gastric cancer) and healthy tissue. In addition, it obtained an F1-score of 0.92 and area under the curve of 0.97 in the classification of early gastric cancer and non-cancerous gastric lesions.

Effective livestock management has become essential owing to an aging workforce and the growth of large-scale farming operations in the agricultural industry. Conventional monitoring methods, primarily reliant on manual observation, are increasingly reaching their limits, necessitating the development of innovative automated solutions. This study developed a system, termed mixed-ELAN, for real-time sow and piglet behavior detection using an extended ELAN architecture with diverse kernel sizes. The standard convolution operations within the ELAN framework were replaced with MixConv using diverse kernel sizes to enhance feature learning capabilities. To ensure high reliability, a performance evaluation of all techniques was conducted using a k-fold cross-validation (k = 3). The proposed architecture was applied to YOLOv7 and YOLOv9, yielding improvements of 1.5% and 2%, with mean average precision scores of 0.805 and 0.796, respectively, compared with the original models. Both models demonstrated significant performance improvements in detecting behaviors critical for piglet growth and survival, such as crushing and lying down, highlighting the effectiveness of the proposed architecture. These advances highlight the potential of AI and computer vision in agriculture, as well as the system’s benefits for improving animal welfare and farm management efficiency. The proposed architecture enhances the real-time monitoring and understanding of livestock behavior, establishing improved benchmarks for smart farming technologies and enabling further innovation in livestock management.

Weight information is important in cattle breeding because it can measure animal growth and be used to calculate the appropriate amount of daily feed. To estimate the weight, we developed an image-based method that does not stress cattle and requires no manual labor. From a 2D image, a mask was obtained by segmenting the animal and background, and weights were estimated using a deep neural network with residual connections by extracting weight-related features from the segmentation mask. Two image segmentation methods, fully and weakly supervised segmentation, were compared. The fully supervised segmentation method uses a Mask R-CNN model that learns the ground truth mask generated by labeling as the correct answer. The weakly supervised segmentation method uses an activation visualization map that is proposed in this study. The first method creates a more precise mask, but the second method does not require ground truth segmentation labeling. The body weight was estimated using statistical features of the segmented region. In experiments, the following performance results were obtained: a mean average error of 17.31 kg and mean absolute percentage error of 5.52% for fully supervised segmentation, and a mean average error of 35.91 kg and mean absolute percentage error of 10.1% for the weakly supervised segmentation.