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Cattle management plays a crucial role in determining the productivity of livestock farms. With the expansion of large-scale livestock operations, it has become increasingly impractical for livestock managers to rely on traditional visual observations for comprehensive monitoring of cattle behaviors, encompassing health and overall welfare. Consequently, the incorporation of automation technology in livestock management is emphasized. The objective of this study is the video-based identification of cattle behavior that can be utilized in automated cattle management systems. With a specific focus on behaviors closely associated with their management, the study employs deep learning-based action classification methods over the commonly used object detection. This approach enables the classification of intricate, repetitive, and slow behaviors that were challenging to detect. Furthermore, a novel method named FlowEQ transform was introduced, incorporating temporal information into the input data. This enhancement proved instrumental in providing valuable insights for inferring cattle behavior, resulting in an impressive 8% improvement in classification performance and achieving a high accuracy rate of 91.5%. The utilization of action classification and the introduction of the innovative FlowEQ transform mark a significant advancement in automated cattle management. This approach is poised to enhance the efficiency of behavior monitoring on livestock farms.

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.

As the population and income levels rise, meat consumption steadily increases annually. However, the number of farms and farmers producing meat decrease during the same period, reducing meat sufficiency. Information and Communications Technology (ICT) has begun to be applied to reduce labor and production costs of livestock farms and improve productivity. This technology can be used for rapid pregnancy diagnosis of sows; the location and size of the gestation sacs of sows are directly related to the productivity of the farm. In this study, a system proposes to determine the number of gestation sacs of sows from ultrasound images. The system used the YOLOv7-E6E model, changing the activation function from sigmoid-weighted linear unit (SiLU) to a multi-activation function (SiLU + Mish). Also, the upsampling method was modified from nearest to bicubic to improve performance. The model trained with the original model using the original data achieved mean average precision of 86.3%. When the proposed multi-activation function, upsampling, and AutoAugment were applied, the performance improved by 0.3%, 0.9%, and 0.9%, respectively. When all three proposed methods were simultaneously applied, a significant performance improvement of 3.5% to 89.8% was achieved.

Recently, the growing demand for meat has increased interest in precision livestock farming (PLF), wherein monitoring livestock behavior is crucial for assessing animal health. We introduce a novel cattle behavior detection model that leverages data from 2D RGB cameras. It primarily employs you only look once (YOLO)v7-E6E, which is a real-time object detection framework renowned for its efficiency across various applications. Notably, the proposed model enhances network performance without incurring additional inference costs. We primarily focused on performance enhancement and evaluation of the model by integrating AutoAugment and GridMask to augment the original dataset. AutoAugment, a reinforcement learning algorithm, was employed to determine the most effective data augmentation policy. Concurrently, we applied GridMask, a novel data augmentation technique that systematically eliminates square regions in a grid pattern to improve model robustness. Our results revealed that when trained on the original dataset, the model achieved a mean average precision (mAP) of 88.2%, which increased by 2.9% after applying AutoAugment. The performance was further improved by combining AutoAugment and GridMask, resulting in a notable 4.8% increase in the mAP, thereby achieving a final mAP of 93.0%. This demonstrates the efficacy of these augmentation strategies in improving cattle behavior detection for PLF.

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

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.

Gastric cancer is a significant health concern, particularly in Korea, and its accurate detection is crucial for effective treatment. However, a gastroscopic biopsy can be time-consuming and may, thus, delay diagnosis and treatment. Thus, this study proposed a gastric cancer diagnostic method, CADx, to facilitate a more efficient image analysis. Owing to the challenges in collecting medical image data, small datasets are often used in this field. To overcome this limitation, we used AutoAugment’s ImageNet policy and applied cut-and-paste techniques using a sliding window algorithm to further increase the size of the dataset. The results showed an accuracy of 0.8317 for T-stage 1 and T-stage 4 image classification and an accuracy of 0.8417 for early gastric cancer and normal image classification, indicating improvements of 7 and 9%, respectively. Furthermore, through the application of test-time augmentation to the early gastric cancer and normal image datasets, the image classification accuracy was improved by 5.8% to 0.9000. Overall, the results of this study demonstrate the effectiveness of the proposed augmentation methods for enhancing gastric cancer classification performance.

Endoscopic specialists performing gastroscopy, which relies on the naked eye, may benefit from a computer-aided diagnosis (CADx) system that employs deep learning. This report proposes utilizing a CADx system to classify normal and abnormal gastric cancer, gastritis, and gastric ulcer. The CADx system was trained using a deep learning algorithm known as a convolutional neural network (CNN). Specifically, Xception, which includes depth-wise separable convolution, was employed as the CNN. Image augmentation was applied to improve the disadvantages of medical data, which are difficult to collect. A class activation map (CAM), an algorithm that visualizes the classified region of interest in a CNN, was used to cut and paste the image area into another image. The CAM-identified lesion location in an abnormal image was augmented by pasting it into a normal image. The normal image was divided into nine equal parts and pasted where the variance difference from the lesion was minimal. Consequently, the number of abnormal images increased by 360,905. Xception was used to train the augmented dataset. A confusion matrix was used to evaluate the performance of the gastroscopy CADx system. The performance criteria were specificity, sensitivity, F1 score, harmonic average of precision, sensitivity (recall), and AUC. The F1 score of the CADx system trained with the original dataset was 0.792 and AUC was 0.885. The dataset augmentation approach using CAM presented in this report is shown to be an effective augmentation algorithm, with performance improved to 0.835, 0.903 in terms of F1 score and AUC respectively.

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

Gastric cancer is the third-most-common cause of cancer-related deaths in the world. Fortunately, it can be detected using endoscopy equipment. Computer-aided diagnosis (CADx) systems can help clinicians identify cancer from gastric diseases more accurately. In this paper, we present a CADx system that distinguishes and classifies gastric cancer from pre-cancerous conditions, such as gastric polyps, gastric ulcers, gastritis, and bleeding. The system uses a deep-learning model, Xception, which involves depth-wise separable convolutions, to classify cancer and non-cancers. The proposed method consists of two steps: Google’s AutoAugment for augmentation and the simple linear iterative clustering (SLIC) superpixel and fast and robust fuzzy C-means (FRFCM) algorithm for image segmentation during preprocessing. These approaches produce a feasible method of distinguishing and classifying cancers from other gastric diseases. Based on biopsy-supported ground truth, the performance metrics of the area under the receiver operating characteristic curve (i.e. Az) are measured on the test sets. Based on the classification results, the Az of the proposed classification model is 0.96, which is 0.06 up from 0.90 which is the Az of the original data. Our methods are fully automated without the manual specification of region-of-interests for the test and with a random selection of images for model training. This methodology may play a crucial role in selecting effective treatment options without the need for a surgical biopsy.

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.

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.