No abstract

Lung diseases can result in acute breathing problems and prevent the human body from acquiring enough oxygen. These diseases, such as pneumonia (P), pleural effusion (Ef), lung cancer, pneumothorax (Pt), pulmonary fibrosis (F), infiltration (In) and emphysema (E), adversely affect airways, alveoli, blood vessels, pleura and other parts of the respiratory system. The death rates of P and lung cancer are higher than those of other typical lung diseases. In visualization examination, chest radiography, such as anterior-posterior or lateral image viewing, is a straightforward approach used by clinicians/radiologists to diagnose and locate possible lung abnormalities rapidly. However, a chest X-ray image of patients may show multiple abnormalities associated with coexisting conditions, such as P, E, F, Pt, atelectasis, lung cancer or surgical interventions, which further complicate diagnosis. In addition, poor-quality X-ray images and manual inspection have limitations in digital image-automated classification. Hence, this study intends to propose a multilayer machine vision classifier to automatically identify the possible class of lung diseases within a bounding region of interest (ROI) on a chest X-ray image. For digital image texture analysis, a two-dimensional (2D) fractional-order convolution (FOC) operation with a fractional-order parameter, v = 0.3-0.5, is used to enhance the symptomatic feature and remove unwanted noises. Then, maximum pooling is performed to reduce the dimensions of feature patterns and accelerate complex computations. A multilayer machine vision classifier with radial Bayesian network and gray relational analysis is used to screen subjects with typical lung diseases. Anterior-posterior chest X-ray images from the NIH chest X-ray database (NIH Clinical Center) are enrolled. For digital chest X-ray images, with K-fold cross-validation, the proposed multilayer machine vision classifier is applied to facilitate the diagnosis of typical lung diseases on specific bounding ROIs, as promising results with mean recall (%), mean precision (%), mean accuracy (%) and mean F1 score of 98.68%, 82.42%, 83.57% and 0.8981, respectively, for assessing the performance of proposed multilayer classifier for rapidly screening lung lesions on digital chest X-ray images.

No abstract

Digital physiological signals in telecare medicine information systems have been widely applied in remote medical applications, such as telecare, tele-examination, and telediagnosis, via computer networking transmission or wireless communication. However, these medical records need to ensure authorization demands in the channel model for human body communication and remote medical servers and enhance the confidentiality, recoverability, and availability of transmission data. Hence, this study proposes a symmetric cryptography scheme with a chaotic map and a multilayer machine learning network (MMLN) to achieve physiological signal infosecurity. A chaotic pseudorandom number generator within specific control parameters can dynamically produce unordered sequence numbers to set the secret keys for a regular secret key update, thereby improving the security of private cipher codes. The chaotic map is quickly iterated to produce a pseudorandom key stream for real-time applications, and the private cipher codes are selected using the initial and specific control parameters at the data emitter and receiver ends. A general regression neural network is used to map the highdimensional input-output pair of cipher codes for substitution and permutation processes. Its adaptive MMLN with an optimization algorithm can rapidly train the random cipher code protocol to achieve an encryptor and a decryptor for a regular encrypted communication. Using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database, 100 electrocardiogram fragments are used to verify the proposed model, and the peak signal-to-noise ratio (PSNR) as a quantitative quality metric is used to evaluate the visual quality after encryption and decryption processes for further diagnosis applications. Experimental results show that the proposed scheme has a higher mean PSNR (35.26  3.77 dB) and shorter mean executing time (0.16  0.01 s) compared with traditional cryptography protocol schemes.

Breast tumor ranks fourth among various cancers in terms of mortality rate in Taiwan, and it is also the most commonly prevalent cancer in females. Early detection of any malignant lesions can increase the survival rate and also decline the mortality rate through current advanced medical therapies. Acoustic radiation force impulse (ARFI) is a new imaging technique for distinguishing breast lesions in the early stage based on localized tissue displacement, which is quantitated by virtual touch tissue imaging (VTI). Digital ARFI-VTI is an initial breast imaging modality and appears to be more effective in women aged > 30 years. Therefore, image enhancement process is a key technique to enhance a lowcontrast image in a region of interest (ROI) for visualizing texture details and morphological features. In this study, two-dimensional (2D) fractional-order convolution, as a 2D sliding filter window (eight filters are selected), is applied to enhance ARFI-VTI images for an accurate extrapolation of lesions in an ROI. Then, the maximum pooling is performed to reduce the dimensions of the feature patterns from 32  32 to 16  16 size. A multilayer machine vision classifier, as a generalized regression neural network (GRNN), is then used to screen subjects with benign or malignant tumors. With a 10fold cross-validation, promising results such as mean recall (%), mean precision (%), mean accuracy (%), and mean F1 score of 92.92  3.43%, 80.42  6.45%, 87.78  2.17%, and 0.8615  0.0495, respectively, are achieved for quantifying the performance of the proposed classifier. Breast tumors visualized on ARFI-VTI images can be useful as digitalized images for rapid screening of malignant from benign lesions by the proposed machine vision classifier.

No abstract

Breast cancer is the most common cancer among women in Taiwan, and the number of breast cancer cases reported annually continues to increase. In 2018, breast cancer ranked fourth in terms of mortality. Early stages (stages 0-2) of malignant breast lesions can be diagnosed during regular screening, and early treatment via advanced medical therapies increases survival rates. Ultrasound imaging, including acoustic radiation force impulse (ARFI) imaging, is the first-line examination technique used to locate breast lesion tissue, which can then be quantitated by virtual touch tissue imaging (VTI). ARFI-VTI elastography is a breast imaging modality that creates two-dimensional (2D) images to visualize the texture details, elasticity, and morphological features of a region of interest (ROI). The 2D Harris corner convolution is applied during digital imaging to remove speckle noise and enhance the ARFI-VTI images for extrapolation of lesion tissue in a ROI. Then, 2D Harris corner convolution, maximum pooling, and random decision forests (RDF) are integrated into a machine vision classifier to screen subjects with benign or malignant tumors. A total of 320 ARFI-VTI images were collected for experiments. In training stages, 122 images were randomly selected to train the RDF-based classifiers and the remaining images were randomly selected for performance evaluation via cross-validation in recalling stages. In a 10-fold cross-validation, promising results with mean sensitivity, mean specificity, and mean accuracy of 86.02%, 87.63%, and 86.97%, respectively, are achieved for quantifying the performance of the proposed classifier. Breast tumors visualized on ARFI-VTI images can be used for rapid screening of malignant or benign lesions by using the proposed machine vision classifier. INDEX TERMS Acoustic radiation force impulse, virtual touch tissue imaging, elastography, harris corner convolution, random decision forest.

Chest X-ray (CXR) images are usually used to identify the causes of patients' symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior-posterior (A-P) views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities, such as pneumothorax (Pt), emphysema (E), infiltration (In), lung cancer (M), pneumonia (P), pulmonary fibrosis (F), and pleural effusion (Ef). However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. In addition, low contrast CXR images and manual inspection restrict automated screening applications. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. A gray relational analysis-based classifier is used to identify the possible classes of the signs and symptoms of lung diseases. For digital CXR images in A-P view, the proposed multilayer machine learning classifier with k-fold cross-validation presents promising results in screening lung diseases and improving screening accuracy rate relative to traditional methods. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334).