Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation.

A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection's progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.

Brain tumor classification in MRI image using convolutional neural network.

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.

Synthesis of Symmetric Bridged Bis-Pyrazolone Based Metal Complex Acid Dyes and their Applications on Leather.

Our present investigation aims at the synthesis and application of new, symmetric bridged bis-pyrazolone based acid dyes. The bis-pyrazolone compounds were accomplished from bis- hydrazine of 4,4'-Diaminostilbene-2,2'-disulfonic acid and ethyl acetoacetate. The bis-pyrazolones have been coupled with diazonium salts of o-hydroxyl aromatic amines which resulted in ligand dyes. The intermediate ligand dyes were treated with 3d transition metals to achieve the targeted metal complex acid dyes. The structures of investigated compounds were confirmed with the help of spectroscopic techniques. Dyes were applied on leather and their application parameters including their light fastness, wash fastness and rubbing fastness were determined. Graphical Abstract Symmetric brymmetric Bridged bis-Pyrazolone based Metal Complex Acid.