Investigation of ConViT on COVID-19 Lung Image Classification and the Effects of Image Resolution and Number of Attention Heads

COVID-19 has been one of the popular foci in the research community since its first outbreak in China, 2019. Radiological patterns such as ground glass opacity (GGO) and consolidations are often found in CT scan images of moderate to severe COVID-19 patients. Therefore, a deep learning model can be trained to distinguish COVID-19 patients using their CT scan images. Convolutional Neural Networks (CNNs) has been a popular choice for this type of classification task. Another potential method is the use of vision transformer with convolution, resulting in Convolutional Vision Transformer (ConViT), to possibly produce on par performance using less computational resources. In this study, ConViT is applied to diagnose COVID-19 cases from lung CT scan images. Particularly, we investigated the relationship of the input image pixel resolutions and the number of attention heads used in ConViT and their effects on the model’s performance. Specifically, we used 512x512, 224x224 and 128x128 pixels resolution to train the model with 4 (tiny), 9 (small) and 16 (base) number of attention heads used. An open access dataset consisting  of 2282 COVID-19 CT images and 9776 Normal CT images from Iran is used in this study. By using 128x128 image pixels resolution, training using 16 attention heads, the ConViT model has achieved an accuracy of 98.01%, sensitivity of 90.83%, specificity of 99.69%, positive predictive value (PPV) of 95.58%, negative predictive value (NPV) of 97.89% and F1-score of 94.55%. The model has also achieved improved performance over other recent studies that used the same dataset. In conclusion, this study has shown that the ConViT model can play a meaningful role to complement RT-PCR test on COVID-19 close contacts and patients

Investigation of ConViT on COVID-19 Lung Image Classification and the Effects of Image Resolution and Number of Attention Heads

COVID-19 has been one of the popular foci in the research community since its first outbreak in China, 2019. Radiological patterns such as ground glass opacity (GGO) and consolidations are often found in CT scan images of moderate to severe COVID-19 patients. Therefore, a deep learning model can be trained to distinguish COVID-19 patients using their CT scan images. Convolutional Neural Networks (CNNs) has been a popular choice for this type of classification task. Another potential method is the use of vision transformer with convolution, resulting in Convolutional Vision Transformer (ConViT), to possibly produce on par performance using less computational resources. In this study, ConViT is applied to diagnose COVID-19 cases from lung CT scan images. Particularly, we investigated the relationship of the input image pixel resolutions and the number of attention heads used in ConViT and their effects on the model’s performance. Specifically, we used 512x512, 224x224 and 128x128 pixels resolution to train the model with 4 (tiny), 9 (small) and 16 (base) number of attention heads used. An open access dataset consisting  of 2282 COVID-19 CT images and 9776 Normal CT images from Iran is used in this study. By using 128x128 image pixels resolution, training using 16 attention heads, the ConViT model has achieved an accuracy of 98.01%, sensitivity of 90.83%, specificity of 99.69%, positive predictive value (PPV) of 95.58%, negative predictive value (NPV) of 97.89% and F1-score of 94.55%. The model has also achieved improved performance over other recent studies that used the same dataset. In conclusion, this study has shown that the ConViT model can play a meaningful role to complement RT-PCR test on COVID-19 close contacts and patients

Regulation of Apoptotic Effects by Erythrocarpine E, a Cytotoxic Limonoid from Chisocheton erythrocarpus in HSC-4 Human Oral Cancer Cells

The aim of this study was to determine the cytotoxic and apoptotic effects of erythrocarpine E (CEB4), a limonoid extracted from Chisocheton erythrocarpus on human oral squamous cell carcinoma. Based on preliminary dimethyl-2-thiazolyl-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays, CEB4 treated HSC-4 cells demonstrated a cytotoxic effect and inhibited cell proliferation in a time and dose dependent manner with an IC50 value of 4.0±1.9 µM within 24 h of treatment. CEB4 was also found to have minimal cytotoxic effects on the normal cell line, NHBE with cell viability levels maintained above 80% upon treatment. Annexin V-fluorescein isothiocyanate (FITC), poly-ADP ribose polymerase (PARP) cleavage and DNA fragmentation assay results showed that CEB4 induces apoptosis mediated cell death. Western blotting results demonstrated that the induction of apoptosis by CEB4 appeared to be mediated through regulation of the p53 signalling pathway as there was an increase in p53 phosphorylation levels. CEB4 was also found to up-regulate the pro-apoptotic protein, Bax, while down-regulating the anti-apoptotic protein, Bcl-2, suggesting the involvement of the intrinsic mitochondrial pathway. Reduced levels of initiator procaspase-9 and executioner caspase-3 zymogen were also observed following CEB4 exposure, hence indicating the involvement of cytochrome c mediated apoptosis. These results demonstrate the cytotoxic and apoptotic ability of erythrocarpine E, and suggest its potential development as a cancer chemopreventive agent

Lumen boundary detection in IVUS medical imaging using structured element

The lumen boundary in the human coronary artery is the contour edge of a blood vessel. The intravascular ultrasound (IVUS) is the medical imaging modality used to view the lumen boundary by the clinicians to detect the coronary artery disease called atherosclerosis. The main problem is to differentiate between the lumen area and the lumen boundary which cannot see clearly. The diameter of the lumen becomes narrowed because of the plaques, lipids and calcium deposits on the artery wall. In this paper, we present the automated segmentation method for detecting the lumen boundary using Otsu threshold, morphological operation and empirical threshold in the IVUS images. We used six types of structured elements to select the best result for automated segmentation of lumen boundary. Forty samples of IVUS images inclusive of the ground truth obtained from the Universitat de Barcelona, Barcelona used in this study. The proposed method segmentation performance measured are Jaccard-Index, Dice Similarity-Index, Hausdorff-Distance, Area Overlapped Error and Percentage Area Difference. The Bland-Altman Plot is used to show the variation between the proposed automatic segmentation area and ground truth area. The structured element of the octagon gave a good result in Hausdorff Distance, and the line gave a better result in Jaccard Index, Percentage Area Distance, Area Overlapped Error, Dice Index and Area Error. The result obtained shows that the segmentation performance of the proposed method is on par with other existing segmentation methods

ECTOPIC EXPRESSION OF OPKN1 GENE IN N. benthamiana ALTER FLOWER SIZE AND DELAY FLOWERING TIME

ABSTRACT Oil Palm Knotted-like 1(OPKN1) gene is a type of homeobox gene that was previously isolated from cDNA library of oil palm's suspension cell culture. Based on semi-quantitative RT-PCR, northern blotting and in situ hybridization analysis, the OPKN1 gene is active in flower meristem suggesting its involvement in flowering phase. Semi-quantitative RT-PCR analysis indicated that OPKN1 gene is active in both male and female flowers and also in abnormal flower suggesting its role in flower morphogenesis process. To further validate the function of this gene, the overexpression transformation vector of OPKN1 gene driven by CaMV 35S constitutive promoter was constructed. The constructed vector was transformed into plant model systems Nicotiana benthamiana via Agrobacterium tumefaciens. Four transgenics lines of N. benthamiana were successfully obtained and analysis of quantitative Real-Time PCR shows that OPKN1 gene was highly overexpressed in all transgenic lines. Overexpression of OPKN1 gene in N. benthamiana transformant has caused reduction in N. benthamiana's flower size and flowering time was also observed to be delayed. Based on this finding, it was suggested that OPKN1 may be involved in flower development and morphogenesis process

Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework

Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade