Online ISSN: 2515-8260

Keywords : Convolutional Neural Networks

Protein Structural Classes Prediction Based On Convolutional Neural Network Classifier with Feature Selection of Hybrid PSO-FA Optimization Approach

Sarneet Kaur; Ashok Sharma; Parveen Singh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 252-265

Protein can be classified in different classes like A (All α), B (All β), C (α+β), and D (α/β). A lot of work has been performed for analyzing the Sub-cellular localization of protein structure. The visualization of protein folding into compact conformation is evaluated. In the present work different algorithms like particle swarm optimization (PSO), Firefly algorithm (FFA) and K-Mean clustering algorithms are used to classify different structures of protein. A Conventional neural network (CNN) classifier is utilized for analyzing and comparing different protein classes in terms of SVM classifier available conventionally in terms of various performance parameters. Near 100 % accuracy, sensitivity, specificity, and MCC values are obtained for class A & class B protein structures. However, somewhat lower values of these parameters are obtained for class C and class D protein structures. CNN classifier proved better than SVM classifier and can be helpful in predicting the protein structures. A hybrid PSO-FFA algorithm is used to extract the features for different classes of protein. Structures of four classes of protein are evaluated in terms of scoring spaces and fitnessvalues.

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

Sankara Sai Sumanth Kota; Anthony Rajesh Reddy Yeruva; Rohit 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.

A Comparative Study On Performance Of Pre-Trained Convolutional Neural Networks In Tuberculosis Detection

Ms.SweetyBakyarani. E; Dr. H. Srimathi; Dr. P.J. Arul Leena Rose

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 4852-4858

India accounts for 26% of the words Tuberculosis population. The WHO’s Global TB Program states that in India, the number of people newly diagnosed with TB increased by 74% when compared to other countries from 1.2 million to 2.2 million between 2013 and 2019. Tuberculosis was and still remains a disease that causes high death rates in the country. Many of these deaths can be easily prevented if diagnosed at an early stage. The easiest, cost-effective and non-invasive method of detecting tuberculosis is through a frontal chest x-ray (CXR). But this requires a radiologist to manually examine and analyse each of the X-ray, considering the heavy patient count this puts a great burden on the resources available. A computer aided diagnosis system can easily mitigate this problem and can greatly help in reducing the cost. In recent times deep learning has made great progress in the field of image classification and has produced remarkable outputs in terms of image classification in various domains. But there still remains a scope for improvements when it comes to Tuberculosis detection. The aim of this study is toapply three pre-trained convolutional neural networks that have proven record in image classification on to publically available CXR dataset and classify CXR’s that manifest tuberculosis and compare their performances. The CNN models that are used on our CXR images dataset as a part of this study are VGG-16 ,VGG-19,AlexNet ,Xception and ResNet-50. Also visualization techniques have been applied to help understand the features whose weights played a role in the classification process. With the help of this system, we can easily classify CXR’s that have active TB and even CXR’s that show mild abnormalities, thus ensuring that high risk patients get the help they require on time.