Management of COVID - 19 with Siddha and Western Medicine - An Integrative Case Report.

Renaissance in acceptance of the Siddha system of medicine in all over India happened during the current scenario of coronavirus disease-2019 (COVID-19) pandemic. The classical texts of Siddha medicine contain descriptions of the symptoms of COVID-19 as a syndrome like definition that may be correlated with KabaSuram. A 49-year-old female residing at New Delhi who got COVID-19 positive with comorbidity of hypothyroidism treated successfully using an integrative treatment plan (Siddha and Allopathic) as per Government-mandated COVID-19 treatment guidelines. The patient developed symptoms such as fever, sore throat, cold, cough with expectoration, difficulty in breathing, chest congestion, and body ache. Initially the patient took Western Medicine (WM) for five days but the symptoms did not subside. After five days an integrated treatment including Siddha medicine (Internal and external medicines) initiated at In Patient ward, Safdarjung Hospital. The health of the patient improved within 3 days and all her symptoms got relieved within 10 days. After completion of treatment, she tested reverse transcription - polymerase chain reaction (RT-PCR) and it was negative on 14th day. Another patient who was admitted with her got COVID-19 positive turned negative only after 30 days as she missed the integrative medicine by probability. The reported case had a prospective follow-up for six months and found to be free of post-COVID complications. Since, this case report based on a single case which shows a positive outcome is incapable of generalizing the conclusion. Further suitable clinical trials need to be conducted to assess its efficacy. The status of the summary is reported as per CAse REport (CARE) guidelines.

CAD system for lung nodule detection using deep learning with CNN.

The early detection of pulmonary nodules using computer-aided diagnosis (CAD) systems is very essential in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning approach to improve the classification accuracy of pulmonary nodules in computed tomography (CT) images. Our proposed CNN-5CL (convolutional neural network with 5 convolutional layers) approach uses an 11-layer convolutional neural network (with 5 convolutional layers) for automatic feature extraction and classification. The proposed method is evaluated using LIDC/IDRI images. The proposed method is implemented in the Python platform, and the performance is evaluated with metrics such as accuracy, sensitivity, specificity, and receiver operating characteristics (ROC). The results show that the proposed method achieves accuracy, sensitivity, specificity, and area under the roc curve (AUC) of 98.88%, 99.62%, 93.73%, and 0.928, respectively. The proposed approach outperforms various other methods such as Naïve Bayes, K-nearest neighbor, support vector machine, adaptive neuro fuzzy inference system methods, and also other deep learning-based approaches.

Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm.

The Lung nodules are very important to indicate the lung cancer, and its early detection enables timely treatment and increases the survival rate of patient. Even though lots of works are done in this area, still improvement in accuracy is required for improving the survival rate of the patient. The proposed method can classify the stages of lung cancer in addition to the detection of lung nodules. There are two parts in the proposed method, the first part is used for classifying normal/abnormal and second part is used for classifying stages of lung cancer. Totally 10 features from the lung region segmented image are considered for detection and classification. The first part of the proposed method classifies the input images with the aid of Naive Bayes classifier as normal or abnormal. The second part of the system classifies the four stages of lung cancer using Neuro Fuzzy classifier with Cuckoo Search algorithm. The results of proposed system show that the rate of accuracy of classification is improved and the results are compared with SVM, Neural Network and Neuro Fuzzy Classifiers.