Application of stacked autoencoder for identification of bone fracture.

This study presents a stacked autoencoder (SAE)-based assessment method which is one of the unsupervised learning schemes for the investigation of bone fracture. Relatively accurate health monitoring of bone fracture requires considering physical interactions among tissue, muscle, wave propagation and boundary conditions inside the human body. Furthermore, the investigation of fracture, crack and healing process without state-of-the-art medical devices such as CT, X-ray and MRI systems is challenging. To address these issues, this study presents the SAE method that incorporates bilateral symmetry of the human legs and low-frequency transverse vibration. To verify the presented method, several examples are employed with plastic pipes, cadaver legs and human legs. Virtual spectrograms, created by applying a short-time Fourier transform to the differences in vibration responses, are employed for image-based training in SAE. The virtual spectrograms are then classified and the fine-tuning is also carried out to increase the accuracy. Moreover, a confusion matrix is employed to evaluate classification accuracy and training validity.

Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks.

Malignant melanoma accounts for about 1-3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.

Novel smart clothing with dry electrode biosensor for real-time automatic diagnosis of cardiovascular diseases.

The incidence of heart disease increases with age. The typical method of monitoring arrhythmia is to use a body patch type sensor with a wet electrode. Even though this sensor is easy to use, it has several disadvantages such as problems caused by wet electrodes in tissues when they are monitored for long periods. Thus, a monitoring sensor integrated into clothes with a dry electrode is proposed. In this study, we developed a smart outdoor shirt equipped with a dry electrode electrocardiogram (ECG) sensor for a cardiac arrhythmia computer-aided diagnosis system. The sensor can be inserted in a console close to the chest, charged, used to communicate wirelessly, and can be connected to a smartphone application. According to experiments, the ECG signals measured by the smart shirt indicated that 97.5 ± 1% of the signals could be measured in an immobile state and at least 85.2 ± 2% of the signals could be measured during movement. In addition, we propose a computer-aided diagnosis system for detecting cardiac arrhythmia. It was determined through experiments that the system can detect arrhythmia with an accuracy of 98.2 ± 2%.