To treat the diseases or injuries of the joints, bones, muscles, and spine in both adult and pediatric imaging the musculoskeletal radiographs bring a significant depth of expertise. Abnormality detection in the musculoskeletal study is backbreaking as more than 1.7 billion people are affected by musculoskeletal condition (BMU, 2017). Hence if we want to create enough opportunity to treat a maximum amount of patients, machine learning and deep learning can play a crucial role. CNN is an excellent deep learning method for image classification and other computer vision tasks. But CNN has exhibited some serious limitations when the images are rotated and deformed. Hence capsule network architecture is introduced in this paper for musculoskeletal radiographs abnormality detection and this capsnet architecture has shown very promising features that can help to vanquish the limitations of CNN. In addition, this capsule network has scored 10% higher kappa score than 169 layer densenet using less training data in the case of musculoskeletal radiographs abnormality detection. This feature of capsule network can help to use deep learning in such cases where an aggregate of a large amount of data is not possible. For image quality investigation, blind image spatial quality evaluator (BRISQUE) and naturalness image quality evaluator (NIQE) scores are measured and it is found that when the pixel size of the resized images are more close to the pixel size of the original images, we get a better approximation. Hence in the case of musculoskeletal radiographs abnormality detection, our method outperforms state-of-the-art method using a less amount of training data.
Since the end of 2019, Novel Corona Virus Disease (COVID-19) has brought about a plethora of unforeseen changes to the world as we know it. Despite our ceaseless fight against it, COVID-19 has claimed millions of lives, and the death toll exacerbated due to its extremely contagious and fast-spreading nature. To control the spread of this highly contagious disease, a rapid and accurate diagnosis can play a very crucial part. Motivated by this context, a parallelly concatenated convolutional block-based capsule network is proposed in this paper as an efficient tool to diagnose the COVID-19 patients from multimodal medical images. Concatenation of deep convolutional blocks of different filter sizes allows to integrate discriminative spatial features by simultaneously changing the receptive field and enhances the scalability of the model. Moreover, concatenation of capsule layers strengthens the model to learn more complex representation by presenting the information in a fine to coarser manner. The proposed model is evaluated on three benchmark datasets in which two of them are chest radiographs datasets and the rest is an ultrasound imaging dataset. The architecture which we have proposed through extensive analysis and reasoning achieved outstanding performance in COVID-19 detection task which signifies the potentiality of the proposed model. The implementation of this proposed method is made publicly available in github.Impact Statement-Suffering from elongated testing time and less sensitivity, the traditional diagnostic test based COVID-19 diagnosis schemes such as Molecular and Antigen tests usually require manual medical image analysis for further investigation. Hence, an automated process is desired to deal with the problem of diagnosing the increasing number of COVID-19 patients worldwide. However, automating the diagnosing process is an onerous task because of the unavailability of high-resolution medical images and the characteristical similarity of COVID-19 and other viral and bacterial diseases that can eventually lead to misinterpretation of data. The proposed scheme introduces a modified capsule network (CapsCovNet) by addressing all of these issues which provides a significant improvement in COVID-19 diagnosis from different imaging methods (Ultrasound Chest X-ray) compared to the previous state-of-the-art models. Moreover, it can be an efficient tool that can facilitate the COVID-19 diagnosis process by providing invaluable information to medical practitioners.
Background: In this research, we investigated the antioxidant phytochemicals, antimicrobial and antibiofilm properties of methanolic leaf extract from Rhazya stricta.Methods: The main components of R. stricta were confirmed through HPLC techniques. The antioxidant activity of methanolic leaf extract was examined by DPPD, ABTS, SOD and Glutathione peroxidase assays.Results: The results showed that the methanolic extract of R. stricta showed better free radical scavenging properties with the IC50 value 74.2 mg/ml by DPPH assay. And the tested extract showed high SOD and peroxidase activities. The HPLC analysis of leaf extract reveal the presence of rutin. Antibacterial results revealed that methanolic leaf extract of R. stricta had higher anti-bacterial activity against multi drug resistant microbes namely, Bacillus subtilis (3.51 mm), Staphylococcus aureus (1.29 mm), Escherichia coli (3.39 mm), Klebsiella pneumoniae (2.03 mm), Pseudomonas aeruginosa (5.73 mm). In addition, the leaf extract showed strong antibiofilm potential against Pseudomonas aeruginosa at 100 µg/ml.Conclusions: Overall, the methanolic leaf extract of R. stricta showed significant antioxidant capacity and highly inhibit the bacterial pathogens.
Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood—microcytic hyperchromic anemia—which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.
Nosocomial infections are frequently caused by bacteria that are resistant to various antibiotics, resulting in the mortality or delayed recovery of hospitalized patients. Several studies have investigated the efficiency of ozone (O3) gas for the disinfection of surfaces to eliminate different nosocomial pathogens. In this study, the efficacy of O3 gas in a heavily contaminated healthcare facility was investigated using a low concentration of FDA-approved and human-safe O3. The total microbial loads on the air conditioning (AC) duct, wall, and tables after 1 month of O3 application were 0 CFU/100 cm 2 , 1 CFU/ 100 cm 2 , and 1 CFU/100 cm 2 , respectively. Moreover, the total microbial loads on the AC duct, wall, and tables 2 months after O3 application were 0 CFU/m 2 , 14 CFU/m 2 , and 1 CFU/m 2 , respectively. Finally, after the third month following O3 application, the microbial loads were 0 CFU/100 cm 2 on the AC duct, 7 CFU/100 cm 2 on the walls, and 54 CFU/100 cm 2 on the tables. Overall results show that O3 gas controlled fungal growth, as it was decreased to minimal levels on some swabbed surfaces or even eliminated on most swabbed medical devices and work surfaces. Moreover, O3 is capable of eradicating nosocomial pathogens present in hidden areas even at low concentrations that match the levels approved by the FDA for human exposure. The study concluded that gaseous O3 can serve as an effective, safe, and cheap disinfectant. O3 could effectively work to eliminate both nosocomial bacteria and mould pathogens.
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