Online ISSN: 2515-8260

Keywords : Convolutional Neural Networks

Improved Convolution Neural Network For Detecting Covid-19 From X-Ray Images

Sankara Sai Sumanth Kota; Anthony Rajesh Reddy; YeruvaRohit Desai; Venubabu Rachapudi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 1221-1230

Coronavirus disease 2019 (COVID-19) is a communicable disease caused by coronavirus 2 (SARS-CoV-2), a severe acute respiratory syndrome. It was first identified in Wuhan, Hubei, China in December 2019 and has contributed to a continuing pandemic. As of early July 2020, more than 10.6 million cases throughout 188 countries around the world were identified culminating in much more than 516,000 deaths. To prevent COVID-19 from spreading among people, an automated detection system needs to be introduced as a fast-alternative diagnosis method. Machine learning algorithms based on radiographic images can be used as mechanism to support decision taking and help radiologists speed up the diagnostic process. This work introduces a new paradigm for automatic detection of COVID-19 using raw X-ray images in the chest. The proposed model with 4 Convolutional Layers, 2 Max Pooling Layers and Drop Outs, is designed to provide reliable diagnostics for binary (COVID vs. No-Findings) and multi-class (COVID vs. No-Findings vs. Pneumonia) diagnosis. Our model provided gives 98.9% Binary Classification accuracy and 85% Multi Classification accuracy.

Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning.

Shrwan Ram; Anil Gupta

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 4789-4815

Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells that cover the nerve cells and control the function of that. The Glioma tumor is classified based on glial cells involved in the Glioma tumor formation. The tumor affects the normal activity of the patients such as loss of memory, difficulties in speech, confuse the identification of objects, and also causes difficulties to maintain the balance of the body. The early detection of Glioma tumor helps healthcare practitioners to suggest a suitable treatment for the disease. The detection of a Glioma tumor is a challenging task. Many types of approaches had been proposed by the researchers and academicians for accurately detecting the  Glioma tumor. Accurately detecting the brain tumor is still a big challenge. Because of recent advances in image processing and computer vision, healthcare professionals are using sophisticated disease diagnostic tools for disorders/disease prediction. The Neurosurgeons and Neuro-Physicians use the magnetic resonance imaging technique to identify multiple brain tumors. The approaches to computer vision play a significant role in the automated identification of different Brain tumors. This research paper explores the Convolutional neural network-based Faster R-CNN approach for the Glioma tumor detection using four pre-trained deep networks such as Alexnet, Resnet18, Resnet50, and Googlenet. The proposed approach of object detection as compared to other R-CNN approaches is more efficient and accurate having higher precision.  The proposed model detects the Glioma tumor with 99.9% accuracy. The pre-trained networks used to train the tumor detection model are Alexnet, Resnet18, and Resnet50, and Googlenet. As compare to Alexnet, resnet18, and Googlenet deep networks, the Resnet50 Pre-trained network performed well with higher accuracy of detection.