A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

2022

Diagnostics

Self Cite

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.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

2022

Diagnostics

Self Cite

“…Because the WSIs in the datasets were collected from different hospitals, and the differences in staining standards and scanning equipment resulted in inconsistencies of staining colours 21. In order to alleviate variations in staining colours, Macenko stain normalisation method22 was applied to normalise the patches, as shown in figure 1B.…”

Section: Materials and Datasetmentioning

confidence: 99%

Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images

Jiang

Huang

et al. 2022

J Clin Pathol

AimsMicroscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for CRC binary classification and localisation in whole slide images (WSIs), and as a computer-aided diagnosis (CAD) to improve the sensitivity and specificity of doctors’ diagnosis.MethodsRepresentative regions of interest (ROI) of each tissue type were manually delineated in WSIs by pathologists. Based on the same coordinates of centre position, patches were extracted at different magnification levels from the ROI. Specifically, patches from low magnification level contain contextual information, while from high magnification level provide important details. A dual-inputs network was designed to learn context and details simultaneously, and self-attention mechanism was used to selectively learn different positions in the images to enhance the performance.ResultsIn classification task, DRSANet outperformed the benchmark networks which only depended on the high magnification patches on two test set. Furthermore, in localisation task, DRSANet demonstrated a better localisation capability of tumour area in WSI with less areas of misidentification.ConclusionsWe compared DRSANet with benchmark networks which only use the patches from high magnification level. Experimental results reveal that the performance of DRSANet is better than the benchmark networks. Both context and details should be considered in deep learning method.

“…In this way, the parameters of the implemented models (COVID-RENets and well-established CNN) are initialized using TL. These models have been fine-tuned using domain-adaptation-based TL on the COVID-19 X-ray dataset [ 42 ], [ 43 ]. We incorporated the hybrid framework where the convolutional layers of TL-based COVID-RENet-1 & 2 and well-established CNN extract COVID-19 image bottleneck features, which are then provided to the SVM classifier for training.…”

Section: Proposed Covid-19 Detection Schemementioning

confidence: 99%

COVID-19 detection in chest X-ray images using deep boosted hybrid learning

Sohail

Computers in Biology and Medicine

et al. 2021

Self Cite

The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist’s authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5-10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society.