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Estrus detection in cattle is an important factor in livestock farming. With timely estrus detection, cattle are artificially fertilized and isolated for safety, which directly affects the productivity of livestock farms. Estrus can be successfully detected by identifying the mating posture of cattle. Therefore, in this paper, we propose the identification of cattle mating posture based on video inputs for prompt estrus detection. A deep learning-based object detection network that focuses on real-time processing with high processing speeds is applied. The use of deep learning-based object detection shows high accuracy, even with noise robustness. The performance of the network is improved through the inclusion of an additional layer and a new activation function. The composition of the additional layer enables training by extracting more features required for object detection. The application of the new activation function, Mish, which has a smoother curve, allows for better generalization and improves the accuracy of the results. The data needed for training were gathered by installing cameras at a livestock farm, and various datasets were used depending on camera placement. The results of this study were verified by the evaluation of four networks using test datasets containing image and video data from different environments. The identification of the mating posture of cattle attained 98.5% precision, 97.2% recall, and 97.8% accuracy.

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.

Dental caries can be characterized and segmented using a computer-aided diagnosis (CADx) system. In most CADx systems, segmentation is a key step in identifying dental cavities at an early stage. With well-segmented lesions, dentists can provide accurate diagnoses. This study proposes a semi-automated method based on superpixel segmentation to identify dental cavities in digital X-ray images. Superpixel segmentation partitions an image into multiple homogeneous segments, wherein the pixels of each segment contain certain comparable characteristics. The proposed method is compared with two previous methods; in one method, an active contour model, or snake, iteratively performs the initial contour, self-examination, and correction of the segmentation results, whereas in the other method, distance regularized level set evolution (DRLSE) eliminates the need for re-initialization, thereby avoiding its induced numerical errors. The proposed method combines DRLSE with superpixels. For the comparison of these three algorithms, 11 authentic teeth, ranging from premolars to molars, were used. With regard to the detection results and classification accuracies, the proposed superpixel DRLSE method received lower error metric scores than the other methods.

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.

Digital elevation model (DEM) integration by optimally fusing multiple measurements having different characteristics can play an important role in estimation and prediction of environmental changes and natural disasters. Multiscale or multiresolution modelling used for this purpose has attracted attention in terms of its rich modelling capability as well as computational efficiency. In particular, a multiscale Kalman smoother (MKS) with tree structure graph can provide a powerful, efficient algorithm based on the Markov property. However, due to the stair-like correlation from the tree structure, unrealistic artefacts can be generated in the fusion result. This is especially prominent on geographic surfaces or ocean surfaces that have naturally smooth characteristics. In this article, a super-resolution (SR) algorithm is applied to remove blocky artefacts. Using the algorithm, new measurements are generated in the area where blocky artefacts can arise. The measurement is then fused with vector-valued MKS....

We are developing a Decision Tree Content-Based Image Retrieval (DTCBIR) CADx scheme to assist radiologists in characterization of breast masses on ultrasound (US) images. Three DTCBIR configurations, including decision tree with boosting (DTb), decision tree with full leaf features (DTL), and decision tree with selected leaf features (DTLs) were compared. For DTb, the features of a query mass were combined first into a merged feature score and then masses with similar scores were retrieved. For DTL and DTLs, similar masses were retrieved based on the Euclidean distance between the feature vector of the query and those of the selected references. For each DTCBIR configuration, we investigated the use of the full feature set and the subset of features selected by the stepwise linear discriminant analysis (LDA) and simplex optimization method, resulting in six retrieval methods. Among the six methods, we selected five, DTb-lda, DTL-lda, DTb-full, DTL-full and DTLs-full, for the observer study. For a query mass, three most similar masses were retrieved with each method and were presented to the radiologists in random order. Three MQSA radiologists rated the similarity between the query mass and the computer-retrieved masses using a ninepoint similarity scale (1=very dissimilar, 9=very similar). For DTb-lda, DTL-lda, DTb-full, DTL-full and DTLs-full, the average Az values were 0.90±0.03, 0.85±0.04, 0.87±0.04, 0.79±0.05 and 0.71±0.06, respectively, and the average similarity ratings were 5.00, 5.41, 4.96, 5.33 and 5.13, respectively. Although the DTb measures had the best classification performance among the DTCBIRs studied, and DTLs had the worst performance, DTLs-full obtained higher similarity ratings than the DTb measures.