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Simple Summary: As global pet ownership rises, managing the health of aging dogs has become a growing concern, particularly due to the high costs and challenges in recognizing early symptoms. In Korea, many casual pet owners struggle to identify abnormal behaviors and address them effectively without expert knowledge. Thus, we propose a pre-diagnosis system for casual dog owners that infers possible diseases from queries based on observed abnormal symptoms. This proposed system, based on a fuzzy inference algorithm, is not to aiming to compete with veterinarians but to alert the casual pet caregiver to monitor abnormal behavior indicative of disease, to be addressed as early as possible. The experimental results verify that the proposed system is sufficiently robust under noisy input, which is very common when non-expert casual owners use this system. While the pet market is continuously rapidly increasing in Korea, pet dog owners feel uncomfortable in coping with pet dog's health problems in time. In this paper, we propose a pre-diagnosis system based on neuro-fuzzy learning, enabling non-expert users to monitor their pets' health by inputting observed symptoms. To develop such a system, we form a disease–symptom database based on several textbooks with veterinarians' guidance and filtering. The system offers likely disease predictions and recommended coping strategies based on fuzzy inference. We evaluated three fuzzy inference algorithms—PFCM-R, FHAL, and MNFL. While PFCM-R achieved high accuracy with clean data, it struggled with noisy inputs. FHAL showed better noise tolerance but lower precision. PFCM-R is a variant of well-known fuzzy unsupervised learner FCM, and FHAL is the hybrid fuzzy inference engine based on Fuzzy Association Memory and a double-layered FCM we developed. To make the system more robust, we propose the multi-layered neuro-fuzzy learner (MNFL) in this paper, which effectively weakens the association strength between the disease and the observed symptoms, less related to the body part on which the abnormal symptoms are observed. In experiments that are designed to examine how the inference system reacts under increasing noisy input from the user, MNFL achieved 98% accuracy even with non-erroneous inputs, demonstrating superior robustness to other inference engines. This system empowers pet owners to detect health issues early, improving the quality of care and fostering more informed interactions with veterinarians, ultimately enhancing the well-being of companion animals. [ABSTRACT FROM AUTHOR]

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope. The third paper, authored by Jeong et al. [[14]], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. 10.3390/app12199801 18 Zhang M., Wang C., Zhu W., Shen Y. An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning. [Extracted from the article]

Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In this paper, we propose a robust automatic segmentation method for inflamed appendix identification to mitigate abovementioned difficulties. We use outlier rejection fuzzy c-means clustering (FCM) algorithm within a double-layered learning structure to extract the target inflamed appendix area. The proposed method extracts the target appendix in 98 cases out of 100 test images, which is far better than traditional FCM, standard outlier FCM, and double-layered learning with FCM in correct extraction rate. Furthermore, we investigate the outlier rejection effect and double layered learning effect by comparing our proposed method with standard double-layered FCM and the standard outlier-rejection FCM. In this comparison, the proposed method exhibits robust segmentation results in accuracy, precision, and recall by 2.5~5.6% over two standard methods in quality with human pathologists' marking as the ground truth. [ABSTRACT FROM AUTHOR]

Ganglion cysts are commonly observed in association with the joints and tendons of the appendicular skeleton. Ultrasonography is the favored modality used to manage such benign tumors, but it may suffer from operator subjectivity. In the treatment phase, ultrasonography also provides guidance for aspiration and injection, and the information regarding the accurate location of the pedicle of the ganglion. Thus, in this paper, we propose an automatic ganglion cyst extracting method based on fuzzy stretching and fuzzy C-means quantization. The proposed method, with its carefully designed image-enhancement policy, successfully detects ganglion cysts in 86 out of 90 cases (95.6%) without requiring human intervention. [ABSTRACT FROM AUTHOR]

Predicting the depth of invasion of superficial esophageal squamous cell carcinomas (SESCCs) is important when selecting treatment modalities such as endoscopic or surgical resections. Recently, the Japanese Esophageal Society (JES) proposed a new simplified classification for magnifying endoscopy findings of SESCCs to predict the depth of tumor invasion based on intrapapillary capillary loops with the SESCC microvessels classified into the B1, B2, and B3 types. In this study, a four-step classification method for SESCCs is proposed. First, Niblack's method was applied to endoscopy images to select a candidate region of microvessels. Second, the background regions were delineated from the vessel area using the high-speed fast Fourier transform and adaptive resonance theory 2 algorithm. Third, the morphological characteristics of the vessels were extracted. Based on the extracted features, the support vector machine algorithm was employed to classify the microvessels into the B1 and non-B1 types. Finally, following the automatic measurement of the microvessel caliber using the proposed method, the non-B1 types were sub-classified into the B2 and B3 types via comparisons with the caliber of the surrounding microvessels. In the experiments, 114 magnifying endoscopy images (47 B1-type, 48 B2-type, and 19 B3-type images) were used to classify the characteristics of SESCCs. The accuracy, sensitivity, and specificity of the classification into the B1 and non-B1 types were 83.3%, 74.5%, and 89.6%, respectively, while those for the classification of the B2 and B3 types in the non-B1 types were 73.1%, 73.7%, and 72.9%, respectively. The proposed machine learning based computer-aided diagnostic system could obtain the objective data by analyzing the pattern and caliber of the microvessels with acceptable performance. Further studies are necessary to carefully validate the clinical utility of the proposed system. [ABSTRACT FROM AUTHOR]

Ganglion cysts are common soft tissue masses of the hand and wrist, and small size cysts are often hypoechoic. Thus, identifying them from ultrasonography is not an easy problem. In this paper, we propose an automatic segmentation method using two artificial intelligence algorithms in sequence. A density based unsupervised learning algorithm called DBSCAN is performed as a front-end and its result determines the number of clusters used in the Fuzzy C-Means (FCM) clustering algorithm for quantification of ganglion cyst object. In an experiment using 120 images, the proposed method shows a higher extraction rate (89.2%) and lower false positive rate compared with FCM when the ground truth is set as the human expert's decision. Such human-like behavior is more apparent when the size of the ganglion cyst is small that the quality of ultrasonography is often not very high. With this fully automatic segmentation method, the operator subjectivity that is highly dependent on the experience of the ultrasound examiner can be mitigated with high reliability. [ABSTRACT FROM AUTHOR]

Background and Aims: Endoscopic ultrasonography (EUS) is a useful diagnostic modality for evaluating gastric mesenchymal tumors; however, differentiating gastrointestinal stromal tumors (GISTs) from benign mesenchymal tumors such as leiomyomas and schwannomas remains challenging. For this reason, we developed a convolutional neural network computer-aided diagnosis (CNN-CAD) system that can analyze gastric mesenchymal tumors on EUS images. Methods: A total of 905 EUS images of gastric mesenchymal tumors (pathologically confirmed GIST, leiomyoma, and schwannoma) were used as a training dataset. Validation was performed using 212 EUS images of gastric mesenchymal tumors. This test dataset was interpreted by three experienced and three junior endoscopists. Results: The sensitivity, specificity, and accuracy of the CNN-CAD system for differentiating GISTs from non-GIST tumors were 83.0%, 75.5%, and 79.2%, respectively. Its diagnostic specificity and accuracy were significantly higher than those of two experienced and one junior endoscopists. In the further sequential analysis to differentiate leiomyoma from schwannoma in non-GIST tumors, the final diagnostic accuracy of the CNN-CAD system was 75.5%, which was significantly higher than that of two experienced and one junior endoscopists. Conclusions: Our CNN-CAD system showed high accuracy in diagnosing gastric mesenchymal tumors on EUS images. It may complement the current clinical practices in the EUS diagnosis of gastric mesenchymal tumors. [ABSTRACT FROM AUTHOR]