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Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. Although review papers are used the systematic review or simple methods to analyse and criticize the anomaly NIDS works, the current review uses a traditional way as a quantitative description to find current gaps by synthesizing and summarizing the data comparison without considering algorithms performance. This paper presents a systematic and meta-analysis study of AI for network intrusion detection systems (NIDS) focusing on deep learning (DL) and machine learning (ML) approaches in network security. Deep learning algorithms are explained in their structure, and data intrusion network is justified based on an infrastructure of networks and attack types. By conducting a meta-analysis and debating the validation of the DL and ML approach by effectiveness, used dataset, detected attacks, classification task, and time complexity, we offer a thorough benchmarking assessment of the current NIDS-based publications-based systematic approach. The proposed method is considered reviewing works for the anomaly-based network intrusion detection system (anomaly-NIDS) models. Furthermore, the effectiveness of proposed algorithms and selected datasets are discussed for the recent direction and improvements of ML and DL to the NIDS. The future trends for improving an anomaly-IDS for continuing detection in the evolution of cyberattacks are highlighted in several research studies.

Ultrasound-based fetal head biometrics measurement is a key indicator in monitoring the conditions of fetuses. Since manual measurement of relevant anatomical structures of fetal head is time-consuming and subject to inter-observer variability, there has been strong interest in finding automated, robust, accurate and reliable method. In this paper, we propose a deep learning-based method to segment fetal head from ultrasound images. The proposed method formulates the detection of fetal head boundary as a combined object localisation and segmentation problem based on deep learning model. Incorporating an object localisation in a framework developed for segmentation purpose aims to improve the segmentation accuracy achieved by fully convolutional network. Finally, ellipse is fitted on the contour of the segmented fetal head using least-squares ellipse fitting method. The proposed model is trained on 999 2-dimensional ultrasound images and tested on 335 images achieving Dice coefficient of$$97.73 \pm 1.32$$. The experimental results demonstrate that the proposed deep learning method is promising in automatic fetal head detection and segmentation.

Ultrasound-based fetal head biometrics measurement is a key indicator in monitoring the conditions of fetuses. Since manual measurement of relevant anatomical structures of fetal head is time-consuming and subject to inter-observer variability, there has been strong interest in finding automated, robust, accurate and reliable method. In this paper, we propose a deep learning-based method to segment fetal head from ultrasound images. The proposed method formulates the detection of fetal head boundary as a combined object localisation and segmentation problem based on deep learning model. Incorporating an object localisation in a framework developed for segmentation purpose aims to improve the segmentation accuracy achieved by fully convolutional network. Finally, ellipse is fitted on the contour of the segmented fetal head using least-squares ellipse fitting method. The proposed model is trained on 999 2-dimensional ultrasound images and tested on 335 images achieving Dice coefficient of$$97.73 \pm 1.32$$. The experimental results demonstrate that the proposed deep learning method is promising in automatic fetal head detection and segmentation.

Background Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc assessment are therefore needed to prevent sight loss. Methods We developed a novel glaucoma detection algorithm that segments and analyses colour photographs to quantify optic nerve rim consistency around the whole disc at 15-degree intervals. This provides a profile of the cup/disc ratio, in contrast to the vertical cup/disc ratio in common use. We introduce a spatial probabilistic model, to account for the optic nerve shape, we then use this model to derive a disc deformation index and a decision rule for glaucoma. We tested our algorithm on two separate image datasets (ORIGA and RIM-ONE). Results The spatial algorithm accurately distinguished glaucomatous and healthy discs on internal and external validation (AUROC 99.6% and 91.0% respectively). It achieves this using a dataset 100-times smaller than that required for deep learning algorithms, is flexible to the type of cup and disc segmentation (automated or semi-automated), utilises images with missing data, and is correlated with the disc size (p = 0.02) and the rim-to-disc at the narrowest rim (p<0.001, in external validation). Discussion The spatial probabilistic algorithm is highly accurate, highly data efficient and it extends to any imaging hardware in which the boundaries of cup and disc can be segmented, thus making the algorithm particularly applicable to research into disease mechanisms, and also glaucoma screening in low resource settings.

The second author, Bryan M. Williams, should also be listed as a corresponding author. Dr. Williams’ email address is: bryan.williams@Liverpool.ac.uk.

Background Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc assessment are therefore needed to prevent sight loss. Methods We developed a novel glaucoma detection algorithm that segments and analyses colour photographs to quantify optic nerve rim consistency around the whole disc at 15-degree intervals. This provides a profile of the cup/disc ratio, in contrast to the vertical cup/disc ratio in common use. We introduce a spatial probabilistic model, to account for the optic nerve shape, we then use this model to derive a disc deformation index and a decision rule for glaucoma. We tested our algorithm on two separate image datasets (ORIGA and RIM-ONE). Results The spatial algorithm accurately distinguished glaucomatous and healthy discs on internal and external validation (AUROC 99.6% and 91.0% respectively). It achieves this using a dataset 100-times smaller than that required for deep learning algorithms, is flexible to the type of cup and disc segmentation (automated or semi-automated), utilises images with missing data, and is correlated with the disc size (p = 0.02) and the rim-to-disc at the narrowest rim (p<0.001, in external validation). Discussion The spatial probabilistic algorithm is highly accurate, highly data efficient and it extends to any imaging hardware in which the boundaries of cup and disc can be segmented, thus making the algorithm particularly applicable to research into disease mechanisms, and also glaucoma screening in low resource settings.

The second author, Bryan M. Williams, should also be listed as a corresponding author. Dr. Williams’ email address is: bryan.williams@Liverpool.ac.uk.

Detecting the locations of the optic disc and fovea is a crucial task towards developing automatic diagnosis and screening tools for retinal disease. We propose to address this challenging problem by investigating the potential of applying deep learning techniques to this field. In the proposed method, simultaneous detection of the centers of the fovea and the optic disc (OD) from color fundus images is considered as a regression problem. A deep multiscale sequential convolutional neural network (CNN) is designed and trained. The publically available MESSIDOR and Kaggle datasets are used to train the network and evaluate its performance. The centers of the fovea and the OD in each image were marked by expert graders as the ground truth. The proposed method achieves an accuracy of 97%, 96.7% for the detection of the OD center and 96.6%, 95.6% for the detection of the foveal center of the MESSIDOR and Kaggle test sets respectively. Our promising results demonstrate the excellent performance of the proposed CNNs in simultaneously detecting the centers of both the fovea and OD without human intervention or handcrafted features. Moreover, we can localize the landmarks of an image in 0.007s. This approach could be used as a crucial part of automated diagnosis systems for better management of eye disease.

In medical imaging, high-resolution can be crucial for identifying pathologies and subtle changes in tissue structure. However, in many scenarios, achieving high image resolution can be limited by physics or available technology. In this paper, we aim to develop an automatic and fast approach to increasing the resolution of Optical Coherence Tomography (OCT) images using the data available, without any additional information or repeated scans. We adapt a fully connected deep learning network for the super-resolution task, allowing multi-scale similarity to be considered, and create a training and testing set of more than 40,000 sample patches from retinal OCT data. Testing our model, we achieve an impressive root mean squared error of 5.847 and peak signal-to-noise ratio (PSNR) of 33.28 dB averaged over 8282 samples. This represents a mean improvement in PSNR of 3.2 dB over nearest neighbour and 1.4 dB over bilinear interpolation. The results achieved so far improve over commonly used fast techniques for increasing resolution and are very encouraging for further development towards fast OCT super-resolution. The ability to increase quickly the resolution of OCT as well as other medical images has the potential to impact significantly on medical imaging at point of care, allowing significant small details to be revealed efficiently and accurately for inspection by clinicians and graders and facilitating earlier and more accurate diagnosis of disease.

The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease.

Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD) to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR); however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC). The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method’s performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two.

In medical imaging, high-resolution can be crucial for identifying pathologies and subtle changes in tissue structure. However, in many scenarios, achieving high image resolution can be limited by physics or available technology. In this paper, we aim to develop an automatic and fast approach to increasing the resolution of Optical Coherence Tomography (OCT) images using the data available, without any additional information or repeated scans. We adapt a fully connected deep learning network for the super-resolution task, allowing multi-scale similarity to be considered, and create a training and testing set of more than 40,000 sample patches from retinal OCT data. Testing our model, we achieve an impressive root mean squared error of 5.847 and peak signal-to-noise ratio (PSNR) of 33.28 dB averaged over 8282 samples. This represents a mean improvement in PSNR of 3.2 dB over nearest neighbour and 1.4 dB over bilinear interpolation. The results achieved so far improve over commonly used fast techniques for increasing resolution and are very encouraging for further development towards fast OCT super-resolution. The ability to increase quickly the resolution of OCT as well as other medical images has the potential to impact significantly on medical imaging at point of care, allowing significant small details to be revealed efficiently and accurately for inspection by clinicians and graders and facilitating earlier and more accurate diagnosis of disease.

Blur is a significant problem in medical imaging which can hinder diagnosis and prevent further automated or manual processing. The problem of restoring an image from blur degradation remains a challenging task in image processing. Semi-blind deblurring is a useful technique which may be developed to restore the underlying sharp image given some assumed or known information about the cause of degradation. Existing models assume that the blur is of a particular type, such as Gaussian, and do not allow for the approximation of images corrupted by other blur types which are not easily incorporated into deblurring frameworks. We present an automated approach to image deconvolution which assumes that the cause of blur belongs to a set of common types. We develop a hierarchical approach with convolutional neural networks (CNNs) to distinguish between blur types, achieving an accuracy of 0.96 across a test set of 900 images, and to determine the blur strength, achieving accuracy of 0.77 across 1500 test images. Given this, we are able to reconstruct the underlying image to mean ISNR of 7.53.

Reliable choroid measurements have become an important diagnostic modality for sight-threatening retinal diseases. However, automatic and accurate segmentation of the choroid remains an unresolved challenge. This paper proposes a novel choroid segmentation method, based on a deep learning algorithm that is capable of quick and accurate image segmentation without user intervention. This is achieved through combining pixel clustering, image enhancement and deep learning. The simple linear iterative clustering (SLIC) algorithm has been applied to extract the superpixels (patches). Next, the extracted patches are then enhanced through increasing contrast of the region of interest. After that, the patches are fed to convolutional neural network for labelling the regions into choroid or non-choroid. Performance of the developed algorithm is assessed using a dataset of 169 enhanced depth imaging optical coherence tomography images. The obtained results demonstrated effectiveness of the proposed segmentation method in terms of accuracy (98.01%).

Blur is a significant problem in medical imaging which can hinder diagnosis and prevent further automated or manual processing. The problem of restoring an image from blur degradation remains a challenging task in image processing. Semi-blind deblurring is a useful technique which may be developed to restore the underlying sharp image given some assumed or known information about the cause of degradation. Existing models assume that the blur is of a particular type, such as Gaussian, and do not allow for the approximation of images corrupted by other blur types which are not easily incorporated into deblurring frameworks. We present an automated approach to image deconvolution which assumes that the cause of blur belongs to a set of common types. We develop a hierarchical approach with convolutional neural networks (CNNs) to distinguish between blur types, achieving an accuracy of 0.96 across a test set of 900 images, and to determine the blur strength, achieving accuracy of 0.77 across 1500 test images. Given this, we are able to reconstruct the underlying image to mean ISNR of 7.53.