Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video ...endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.
Influenza virus and Streptococcus pneumoniae bacteria affect millions of people with high mortality rates. SARS-CoV-2, the virus that causes COVID-19, has posed a severe global threat in the 21st ...century. Bioinformatics tools have evolved as an effective platform for combating the disease in the pandemic since the release of SARS-COV-2 viral genome data. Identification of common genes using bioinformatics could reveal the genes involved in the cohesive interaction among diseases such as Influenza, Streptococcus pneumoniae, and Coronavirus. The primary purpose of this study is to trace genetic variants of three significant ailments and build the Protein-Protein Interaction (PPI) and Protein-Drug Interaction (PDI) networks, the Co-expression network, and the Structural Interaction network. We investigated a computational analysis of the target disorders' PPI and PDI networks. We retrieved the related genes for these three diseases from the National Center for Biotechnology Information gene repository. After recovering genes, we conducted an investigation that involved preprocessing, filtering, sorting, and gene mining on the collected genetic data for Influenza, Streptococcus pneumoniae, and Coronavirus using R to identify common associated genes through a reduction process. According to the findings, more than 80% of the genes acquired from several gene databases are responsible for three diseases. The number of genes identified for the three illnesses was reduced to 17% during preprocessing and screening and finally extracted 1% with the R toolkit. The intersection process decreases the commonly linked genes employed in gene extraction. The computational analysis of this study reveals twenty-one (21) common genes among the diseases. This information could be valuable for researchers in developing new therapeutic compounds that could help to reduce the impact of the three diseases in the future.
Waste management leads to the demolition of waste conducted by recycling and landfilling. Deep learning and the Internet of things (IoT) confer an agile solution in classification and real-time data ...monitoring, respectively. This paper reflects a capable architecture of the waste management system based on deep learning and IoT. The proposed model renders an astute way to sort digestible and indigestible waste using a convolutional neural network (CNN), a popular deep learning paradigm. The scheme also introduces an architectural design of a smart trash bin that utilizes a microcontroller with multiple sensors. The proposed method employs IoT and Bluetooth connectivity for data monitoring. IoT enables control of real-time data from anywhere while Bluetooth aids short-range data monitoring through an android application. To examine the efficacy of the developed model, the accuracy of waste label classification, sensors data estimation, and system usability scale (SUS) are enumerated and interpreted. The classification accuracy of the proposed architecture based on the CNN model is 95.3125%, and the SUS score is 86%. However, this smart system will be adjustable to household activities with real-time waste monitoring.
The gastrointestinal polyp (GIP) is the abnormal growth of tissues in digestive organs. Identifying these polyps from endoscopy video or image is a tremendous task to reduce the future risk of ...gastrointestinal cancer. This paper proposes a proper diagnosis method of polyp using a fusion of contourlet transform and fine-tuned VGG19 pre-trained model from enhanced endoscopic 224 × 224 patch images. This study has used different fine-tuned models (Alexnet, ResNet50, VGG16, VGG19) as well as a few scratch models while fine-tuned VGG19 works better. Also, this research has used Principal Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (MRMR) dimensionality reduction methods to collect the intuitive features for classification. In Support Vector Machine (SVM) based polyp detection, the prior method (PCA) performs better. Besides, a proposed algorithm marks polyp region from identified polyp patches and uses a binning strategy to process video. A set of experiments are performed on standard public data sets and found comparative improved performance with an accuracy of 99.59%, sensitivity of 99.74% and specificity of 99.44%. This work can be instrumental for the radiologist for diagnosis of polyps during real-time endoscopy.
Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an ...intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%.
Novel Coronavirus with its highly transmittable characteristics is rapidly spreading, endangering millions of human lives and the global economy. To expel the chain of alteration and subversive ...expansion, early and effective diagnosis of infected patients is immensely important. Unfortunately, there is a lack of testing equipment in many countries as compared with the number of infected patients. It would be desirable to have a swift diagnosis with identification of COVID-19 from disease genes or from CT or X-Ray images. COVID-19 causes flus, cough, pneumonia, and lung infection in patients, wherein massive alveolar damage and progressive respiratory failure can lead to death. This paper proposes two different detection methods – the first is a Gene-based screening method to detect Corona diseases (Middle East respiratory syndrome-related coronavirus, Severe acute respiratory syndrome coronavirus 2, and Human coronavirus HKU1) and differentiate it from Pneumonia. This novel approach to healthcare utilizes disease genes to build functional semantic similarity among genes. Different machine learning algorithms - eXtreme Gradient Boosting, Naïve Bayes, Regularized Random Forest, Random Forest Rule-Based Model, Random Ferns, C5.0 and Multi-Layer Perceptron, are trained and tested on the semantic similarities to classify Corona and Pneumonia diseases. The best performing models are then ensembled, yielding an accuracy of nearly 93%. The second diagnosis technique proposed herein is an automated COVID-19 diagnostic method which uses chest X-ray images to classify Normal versus COVID-19 and Pneumonia versus COVID-19 images using the deep-CNN technique, achieving 99.87% and 99.48% test accuracy. Thus, this research can be an assistance for providing better treatment against COVID-19.
In medical ultrasound imaging, speckle noise is an important property since it usually involves worsening the image resolution along with contrast, thus reducing the diagnostic significance of the ...imaging modality. The "RobustDespeckling" method, which has been proposed in this study to reduce speckle noise, involves extracting the noise-only signal from the image signal through applying the bidimesional empirical mode decomposition (BEMD) method and wavelet transform (WT) in the encapsulated way. Then, wavelet coefficients are thresholded to filter the noisy coefficients by an optimal threshold parameter that is estimated from kernel fisher discriminant analysis (KFDA). Finally, BEMD and WT processes are employed to obtain a de-noised image. The investigation is conducted by way of upsampling and downsampling operations of ultrasound images to analyze the efficiency of different de-noising techniques including the proposed “RobustDespeckling” method. The visual observation and statistical analysis signify that the “RobustDespeckling” method is robust in minimizing the speckle noise of ultrasound images over existing de-noising techniques. Consequently, the method is more competent to preserve the clarity of the edge without losing the significant image detail.
Insulin pen devices and disposable plastic insulin syringes are two common tools for insulin administration. This study aims to compare the simplicity, convenience, safety, and cost-effectiveness of ...insulin pens versus syringe devices in patients with type 2 diabetes mellitus (T2DM).
A cross-sectional study was conducted at 14 diabetes clinics throughout Bangladesh from November 2021 to April 2022 among adults with T2DM injecting insulin by pen devices or disposable insulin syringes at least once a day for at least one year by purposive sampling. The simplicity, convenience, and safety of insulin devices were assessed using a structured questionnaire, and the study subjects were scored based on their answers; higher scores indicated a poorer response. Total scores for simplicity, convenience, and safety were obtained by adding the scores for relevant components. Their average monthly medical expense and cost of insulin therapy were recorded. The median values of the total scores and monthly expenses were compared between pen devices and disposable syringe users.
737 subjects were evaluated; 406 were pen users, and 331 were vial syringe users. The pen users had lower median scores for simplicity 6.0 (5.0-8.0) vs. 7.0 (5.0-9.0), p = 0.002, convenience 4.0 (3.0-6.0) vs. 5.0 (4.0-6.0), p < 0.001, and safety 7.0 (6.0-8.0) vs. 7.0 (6.0-9.0), p = 0.008 than vial syringe users. Pen devices were more expensive than vial syringes in terms of average medical expense per month BDT 5000 (3500-7000) vs. 3000 (2000-5000), p < 0.001, the total cost of insulin therapy per month BDT 2000 (1500-3000) vs. 1200 (800-1700), p < 0.001 and cost per unit of insulin used BDT 2.08 (1.39-2.78) vs. 0.96 (0.64-1.39), p < 0.001. Non-significant differences in favor of pens were observed in HbA1c levels 8.7 (7.8-10) vs. 8.9 (7.9-10)%, p = 0.607 and proportions of subjects having HbA1c < 7% (6.9 vs. 6.3%, p = 0.991).
Insulin pens are simpler, more convenient, and safe but more expensive than vial syringes. Glycemic control is comparable between pen and syringe users. Long-term follow-up studies are needed to determine the clinical and economic impacts of such benefits of insulin pens.
LoRa (Long-Range) has become the Deoxyribo Nucleic Acid (DNA) of the Internet of things (IoT) for equipping smart solutions. Home automation is responsible for providing a safe and stylish home. This ...paper proposes a capable architecture of home automation for both short-range and long-range utilizing multiple communication technologies, namely LoRaWAN, server-based LoRa gateway, and Bluetooth connectivity. This integrated system effectively controls distinct types of home appliances and keeps smart management among all the electronics components. A regular user can easily manage these unified systems by using an Android application. This paper also presents experimental data analysis. The results and discussion section provide a set of experiments like estimated transmission delay calculation for LoRa, Wi-Fi, and Bluetooth, a coverage area calculation for LoRa with RSSI and SNR values, and a System Usability Scale (SUS). The scheme has achieved a SUS score of 93%. However, the proposed architecture can be called an outright package for smart home and will be very workable, abuzz, and handy.
The “BDWaste” dataset contains two significant categories of waste, namely digestible and indigestible, in Bangladesh. Each category represents 10 distinct species of waste. The digestible categories ...are sugarcane husk, fish ash, potato peel, paper, mango peel, rice, shell of malta, lemon peel, banana peel, and egg shell. On the other hand, the indigestible species are polythene, cans, plastic, glass, wire, gloves, empty medicine packets, chip packets, bottles, and masks. The research uploaded the primarily collected dataset on Mendeley, and the dataset contains a total of 2497 raw images, of which 1234 were digestible and 1263 belonged to indigestible species. Each species is stored in a fixed file based on its name and categories. Also, each species contains an indoor (with a visible surface) and an outdoor (with a surface that can be seen generally) image. The dataset is free from any blurry, dark, noisy, or invisible images. The research also performed waste classification with pre-trained convolutional neural network models such as MobileNetV2 and InceptionV3. The research found the highest accuracy of 96.70% in the indigestible waste classification and 99.70% in the digestible waste classification. The researchers presume that this data can be used in the future in different types of research, such as sustainable development, sustainable environments, agricultural development, recycling processes, and other computer vision-based applications.
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