Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.

Recently, thermal cameras are being widely used in various fields, such as intelligent surveillance, biometrics, and health monitoring. However, the high cost of the thermal cameras poses a challenge in terms of purchase. Additionally, thermal images have an issue pertaining to blurring caused by object movement, camera movement, and camera focus settings. There have been very few studies on image restoration centered around thermal images to address such problems. Moreover, it is important to increase the processing speed of image restoration methods to jointly conduct with methods such as action recognition and object tracking that use temporal information from thermal videos. However, no study has been conducted on simultaneously performing super-resolution reconstruction and deblurring using thermal images. Furthermore, existing studies on object detection using thermal images have errors owing to the incapability in distinguishing reflections on the surrounding ground or wall due to the heat radiated from the object. To address such issues, this study proposes a deep learning-based thermal image restoration method that simultaneously performs super-resolution reconstruction and deblurring. According to recent development of deep learning, generative adversarial network (GAN)-based methods which have ability to preserve texture details in images, and yield sharper and more plausible textures than classical feed forward encoders show success in image-to-image translation tasks. Considering the advantages of GAN, we propose a deblur-SRRGAN for thermal image reconstruction. In addition, we propose a light-weighted Mask R-CNN for object detection in the reconstructed thermal image. For the input, we employ an image processing method that converts 1-channel thermal images (often used in the existing studies) into 3-channel images. The results of the experiments conducted using self-collected databases and an open database demonstrate that our method outperforms the state-of-the-art methods.

Human action recognition using a camera-based surveillance system remains a challenging task. In particular, action recognition is difficult when a human is not visible in an image captured in a dark environment. The existing studies have utilized near-infrared (NIR) and thermal cameras to solve this problem. Compared to NIR cameras, thermal cameras enable long- and short-distance objects to be visible without an additional illuminator. However, thermal cameras have two major disadvantages: a halo effect and a temperature similarity. A halo effect occurs around an object with a high temperature. In a human object, such a halo effect is similar to a shadow under the body area. It is more difficult to segment a human area from an image with a halo effect. Moreover, if the background and human object have similar temperatures, it becomes more difficult to segment the human area. These disadvantages influence not only the accuracy of the segmentation of the human area but also the performance of human action recognition. Unfortunately, no studies have considered these issues. To address these problems, this study proposes the cycle-consistent generative adversarial network (CycleGAN)-based methods for removing halo effects from thermal images and restoring the areas of the human bodies. In addition, this study also considered a method for creating a skeleton image from a thermal image to analyze body movements. To extract more spatial and temporal features from skeleton image sequences thus created, a method for human action recognition that combines a convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In an experiment using an open database (Dongguk activities & actions database (DA&A-DB2)), the proposed method demonstrated a better performance than the existing methods.

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.