Skin Lesion Classification Using Hybrid Convolutional Neural Network with Edge, Color, and Texture Information

Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 different features were extracted based on the edge, color, and texture features of the skin lesion image. For the edge features, a line-segment-type analysis algorithm was used, wherein the visual information of a dermoscopic image was precisely analyzed in terms of the units of pixels and was transformed into a structured pattern. Regarding the color features of skin lesions, the dermoscopic image was transformed into multiple color models, and the features were acquired by analyzing histograms showing information regarding the distribution of pixel intensities. Subsequently, texture features were extracted by applying the well-known Law’s texture energy measure algorithm. Feature data (50 × 256) generated via the feature extraction process above were used to classify skin lesions via a one-dimensional (1D) convolution layer-based classification model. Because the architecture of the designed model comprises parallel 1D convolution layers, fine-grained features of the dermoscopic image can be identified using different parameters. To evaluate the performance of the proposed method, datasets from the 2017 and 2018 International Skin Imaging Collaboration were used. A comparison of results yielded by well-known classification models and other models reported in the literature show the superiority of the proposed model. Additionally, the proposed method achieves an accuracy exceeding 88%

Development of Smart Blocks for Cognitive Rehabilitation of Patients with Dementia

The increase in the elderly population increases the incidence of geriatric diseases such as cancer, stroke, heart disease, and dementia. Cognitive impairment and a debilitated memory ability decreases the quality of life of patients owing to the associated economical and physical problems, and increases the burden on their family. The impairment of cognitive function due to dementia can be reduced through repeated and systematic training; however, this process can lead to disinterest and loss of concentration. Toys can stimulate various parts of the brain by inducing patient's finger exercise, and various rehabilitative techniques based on toys are being studied by researchers. In this study, we develop a smart cognitive rehabilitation block(LED blocks, 8x8 dot matrix blocks, speaker blocks, signage blocks, PWM switch blocks, 7-segment blocks), and establish a cognitive rehabilitation platform for patient management and develop cognitive rehabilitation contents.N