12 research outputs found
Deep Neural Networks based Meta-Learning for Network Intrusion Detection
The digitization of different components of industry and inter-connectivity
among indigenous networks have increased the risk of network attacks. Designing
an intrusion detection system to ensure security of the industrial ecosystem is
difficult as network traffic encompasses various attack types, including new
and evolving ones with minor changes. The data used to construct a predictive
model for computer networks has a skewed class distribution and limited
representation of attack types, which differ from real network traffic. These
limitations result in dataset shift, negatively impacting the machine learning
models' predictive abilities and reducing the detection rate against novel
attacks. To address the challenges, we propose a novel deep neural network
based Meta-Learning framework; INformation FUsion and Stacking Ensemble
(INFUSE) for network intrusion detection. First, a hybrid feature space is
created by integrating decision and feature spaces. Five different classifiers
are utilized to generate a pool of decision spaces. The feature space is then
enriched through a deep sparse autoencoder that learns the semantic
relationships between attacks. Finally, the deep Meta-Learner acts as an
ensemble combiner to analyze the hybrid feature space and make a final
decision. Our evaluation on stringent benchmark datasets and comparison to
existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91,
Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of
0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset.
These promising results indicate the strong generalization capability and the
potential to detect network attacks.Comment: Pages: 15, Figures: 10 and Tables:
A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei
COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN
COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images
Transformers, due to their ability to learn long range dependencies, have
overcome the shortcomings of convolutional neural networks (CNNs) for global
perspective learning. Therefore, they have gained the focus of researchers for
several vision related tasks including medical diagnosis. However, their
multi-head attention module only captures global level feature representations,
which is insufficient for medical images. To address this issue, we propose a
Channel Boosted Hybrid Vision Transformer (CB HVT) that uses transfer learning
to generate boosted channels and employs both transformers and CNNs to analyse
lymphocytes in histopathological images. The proposed CB HVT comprises five
modules, including a channel generation module, channel exploitation module,
channel merging module, region-aware module, and a detection and segmentation
head, which work together to effectively identify lymphocytes. The channel
generation module uses the idea of channel boosting through transfer learning
to extract diverse channels from different auxiliary learners. In the CB HVT,
these boosted channels are first concatenated and ranked using an attention
mechanism in the channel exploitation module. A fusion block is then utilized
in the channel merging module for a gradual and systematic merging of the
diverse boosted channels to improve the network's learning representations. The
CB HVT also employs a proposal network in its region aware module and a head to
effectively identify objects, even in overlapping regions and with artifacts.
We evaluated the proposed CB HVT on two publicly available datasets for
lymphocyte assessment in histopathological images. The results show that CB HVT
outperformed other state of the art detection models, and has good
generalization ability, demonstrating its value as a tool for pathologists
A survey of the Vision Transformers and its CNN-Transformer based Variants
Vision transformers have recently become popular as a possible alternative to
convolutional neural networks (CNNs) for a variety of computer vision
applications. These vision transformers due to their ability to focus on global
relationships in images have large capacity, but may result in poor
generalization as compared to CNNs. Very recently, the hybridization of
convolution and self-attention mechanisms in vision transformers is gaining
popularity due to their ability of exploiting both local and global image
representations. These CNN-Transformer architectures also known as hybrid
vision transformers have shown remarkable results for vision applications.
Recently, due to the rapidly growing number of these hybrid vision
transformers, there is a need for a taxonomy and explanation of these
architectures. This survey presents a taxonomy of the recent vision transformer
architectures, and more specifically that of the hybrid vision transformers.
Additionally, the key features of each architecture such as the attention
mechanisms, positional embeddings, multi-scale processing, and convolution are
also discussed. This survey highlights the potential of hybrid vision
transformers to achieve outstanding performance on a variety of computer vision
tasks. Moreover, it also points towards the future directions of this rapidly
evolving field.Comment: Pages: 58, Figures: 1