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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.

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.

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.

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.