Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal vali-dation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% - 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% - 19%). The traditional ma-chine learning model, CNN performed the best on the external dataset (accu-racy 88%) in comparison to the deep learning models, indicating that a light-weight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19

FORMULATION AND EVALUATION OF FLOATING DRUG DELIVERY OF CEFOTAXIME USING RAFT FORMING APPROACH

The cefotaxime is a broad-spectrum cephalosporin antibiotic. It is mainly used in the treatment of bacterial infections. Cefotaxime is a suitable candidate for controlled release administration due to its short elimination time 1 hour. The main aim of the present investigation is to increase the gastric residence time by preparing floating drug delivery by using raft forming approach thereby improving bioavailability. The prepared Cefotaxime floating drug delivery by using raft forming approach were evaluated for hardness, weight variation, thickness, friability, drug content uniformity, total floating time, In-vitro dissolution studies and buoyancy lag time. Floating tablets were formulated using direct compression technique. Various polymers are used in the formulation they Microcrystalline cellulose used as binder, HPMC K15M, Guargum used as hydrophilic polymers, Chitosan, Sodium bicarbonate was incorporated as an effervescent substance, Sodium alginate used as viscous gel forming agent, Magnesium streate used as lubrication, talc was used as diluent. The formulated Cefotaxime tablet to be evaluated the following parameters as follow Weight variation (mg), Hardness, Thickness, Friability, Drug content uniformity, Floating lag time, the in vitro cumulative amount of drug released was shown the F7 is 99.28% within 45 minutes the comparative studies with marketed formulations F7 show the better results. Keywords: Cefotaxime, Direct compression, Raft forming, floating drug delivery system