Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

he acoustic emission technique has been applied successfully for the identification, characterization, and localization of deformations in civil engineering structures. Numerous localization techniques, such as Modal Acoustic Emission, Neural Networks, Beamforming, and Triangulation methods with or without prior knowledge of wave velocity, have been presented. Several authors have conducted in-depth research in the localization of AE sources. However, existing review papers do not focus on the performance evaluation of existing source localization techniques. This paper discusses these techniques based on the number of sensors used and the geometry of the structures of interest. Furthermore, it evaluates them on the basis of their performance. At the end of this paper, a comparative analysis of existing methods has been presented based on their basic principles, key strengths, and limitations. A deep learning circular sensor cluster-based solution has the potential to provide a low-cost reliable localization solution for acoustic emission sources.

Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods.

World-wide incidence rate of prostate cancer has progressively increased with time especially with the increased proportion of elderly population. Early detection of prostate cancer when it is confined to the prostate gland has the best chance of successful treatment and increase in surviving rate. Prostate cancer occurrence rate varies over the three prostate regions, peripheral zone (PZ), transitional zone (TZ), and central zone (CZ) and this characteristic is one of the important considerations is development of segmentation algorithm. In fact, the occurrence rate of cancer PZ, TZ and CZ regions is respectively. at 70-80%, 10-20%, 5% or less. In general application of medical imaging, segmentation tasks can be time consuming for the expert to delineate the region of interest, especially when involving large numbers of images. In addition, the manual segmentation is subjective depending on the expert's experience. Hence, the need to develop automatic segmentation algorithms has rapidly increased along with the increased need of diagnostic tools for assisting medical practitioners, especially in the absence of radiologists. The prostate gland segmentation is challenging due to its shape variability in each zone from patient to patient and different tumor levels in each zone. This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. The review coverage includes the initial screening and imaging techniques, image pre-processing, segmentation techniques based on machine learning and deep learning techniques. Particular attention is given to different loss functions used for training segmentation based on deep learning techniques. Besides, a summary of publicly available prostate MRI image datasets is also provided. Finally, the future challenges and limitations of current deep learning-based approaches and suggestions of potential future research are also discussed.

In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder–decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to $92.8\%$ . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.

The World Health Organization (WHO) has reported a continuous rise in the prevalence of autism worldwide, in which 1 in 160 children in the world has ASD. The problem in ASD treatment has no definite cure, and one possible option is to control the disorder's progress. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. The purpose of this study is to present several deep learning models to diagnose ASD based on temporal dynamic features of fMRI data and improve the classification results on a sample of data. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as Googlenet, DenseNet201, Resnet18, and Resnet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbors (KNN). Finally, the performance of each model is evaluated based on accuracy, sensitivity, and specificity metrics. The best results obtained from the KNN classifier with DenseNet201 as a pre-trained model are accuracy 85.9%, sensitivity 79.3%, specificity 92.6%. Compared with previous studies, it is concluded that the proposed model can be considered as an efficient tool for the diagnosis of ASD. From another perspective, the proposed method can be applied to analyzing rs-fMRI data related to brain disorders.