Information technology has been actively utilized in the field of imaging diagnosis using artificial intelligence (AI), which provides benefits to human health. Readings of abdominal hemorrhage lesions using AI can be utilized in situations where lesions cannot be read due to emergencies or the absence of specialists; however, there is a lack of related research due to the difficulty in collecting and acquiring images. In this study, we processed the abdominal computed tomography (CT) database provided by multiple hospitals for utilization in deep learning and detected abdominal hemorrhage lesions in real time using an AI model designed in a cascade structure using deep learning, a subfield of AI. The AI model was used a detection model to detect lesions distributed in various sizes with high accuracy, and a classification model that could screen out images without lesions was placed before the detection model to solve the problem of increasing false positives owing to the input of images without lesions in actual clinical cases. The developed method achieved 93.22% sensitivity and 99.60% specificity.

In this paper, a new method for individual tooth segmentation was proposed. The proposed method is composed of enhancement and extraction of boundary and seed of watershed algorithm using trisection areas by morphological characteristic of teeth. The watershed algorithm is one of the conventional methods for tooth segmentation; however, the method has some problems. First, molar region detection ratio is reduced because of oral structure features that is low intensities in molar region. Second, inaccurate segmentation occurs in incisor region owing to specular reflection. To solve the problems, the trisection method using morphological characteristic was proposed, where three tooth areas are made using ratio of entire tooth to each tooth. Moreover, the enhancement is to improve the intensity of molar using the proposed method. In addition, boundary and seed of watershed are extracted using trisection areas applied other parameters each area. Finally, individual tooth segmentation was performed using extracted boundary and seed. Furthermore, the proposed method was compared with conventional methods to confirm its efficiency. As a result, the proposed method was demonstrated to have higher detection ratio, better over segmentation, and overlap segmentation than conventional methods.

Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time–frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time–frequency path attempts to compensate for the spectral details via a spectral extension block. These two paths enhance temporal and spectral features via mask functions modeled as LSTM, respectively, offering a comprehensive approach to speech enhancement. Experimental results show that the proposed dual-path LSTM network consistently outperforms conventional single-domain speech enhancement methods in terms of speech quality and intelligibility.

In this paper, we propose a new speech enhancement algorithm based on wavelet packet decomposition and mask filtering. In the traditional mask filtering such as ideal binary mask (IBM), the basic idea is to classify speech components as target signal and non-speech components as background noises. However, speech and non-speech components cannot be well separated in target signal and background noise. Therefore, the IBM has residual noise and signal loss. To overcome this problem, the proposed algorithm used semi-soft mask filter to exponentially increase. The semi-soft mask minimizes signal loss and the exponential filter removes residual noise. We performed experiments using various types of speech and noise signals, and experimental results show that the proposed algorithm achieves better performances than the traditional other speech enhancement algorithms.