Online ISSN: 2515-8260

Keywords : Feature Extraction


ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION

S. Nithyaselvakumari; M.C. Jobin Christ; B A Gowri Shankar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2144-2155

Cardiac arrhythmia can be identified using abnormal electrical activity of heart, this is a great menace to humans. In order to diagnose cardiac problems ECG signal is widely used. When the background noise is rejected from the ECG signal we obtain a QRS component. This QRS component consists of high frequency and high energy waves that are very easy to detect and study. Once QRS component is obtained, it is further spited into various classes that can aid in diagnosing the abnormalities. Previously extracted features are compared to find the heart abnormalities. In this paper Feed-Forward neural network is selected and data base are used to store and analyze the data.

Identification and Detection of Plant Diseases by Convolutional Neural Networks

A. Iyswariya; V. Ramkumar; Sarvepalli Chandrasekhar; Yaddala Chandrasekhar Reddy; Vunnam Sai Tathwik; V.Praveen Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2200-2205

Agribusiness is the foundation of Indian economy. Plant health and food safety goes hand in hand. The health of green plants is of vital importance to everyone.Plant diseases being an impairment to the normal state of a plant, it interrupts or modifies plants vital functions. The proposed system helps in identification of plant disease and provides remedies that can be used as a defense mechanism against the disease. The database obtained from the Internet is properly segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy and confidence level of the project .Then using training data we will train our classifier and then output will be predicted with optimum accuracy. We use Convolution Neural Network (CNN) which comprises of different layers are used for prediction.CNNs provide unparalleled performance in tasks related to the classification and detection of crop diseases. They are able to manage complex issues in difficult imaging conditions A prototype drone model is also designed which can be used for live coverage of large agricultural fields to which a high resolution camera is attached and will capture images of the plants which will act as input for the software, based of which the software will tell us whether the plant is healthy or not. With our code and training model we have achieved an accuracy level of 78%. Our software gives us the name of the plant species with its confidence level and also the remedy that can be taken as a cure.

Automated Identification of Glaucoma from Fundus Images using Deep learning Techniques

Ajitha S; Dr. M V Judy; Dr. Meera N; Dr. Rohith N

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5449-5458

Glaucoma has arisen as the one of the main sources of visual impairment. A typical technique for diagnosing glaucoma is through assessment optic nerve head by an experienced ophthalmologist. This methodology is arduous and burns-through a lot of time. Despite the fact that the analysis of this infection has not yet been discovered, the period of primary identification can preserve from the glaucoma. Subsequently, customary glaucoma screening is basic and suggested. The issue can be settled by applying machine learning techniques for glaucoma detection. We present an automated glaucoma screening framework using a pre-trained Alexnet model with SVM classifier to enhance the classification accuracy . In this study, we used three publicly available dataset as HRF, Origa and Drishti_GS1 dataset. The proposed model achieved the image classification accuracy of 91.21%. This study showed that using pre-trained CNN with SVM for glaucoma detection showed greater accuracy in automatic image classification than just CNN or SVM.

Opinion Mining on Customer Product Reviews Using Supervised Machine Learning Techniques

Sivakumar A; Jagadeesh Babu S; Sathya Vignesh R; Shyam M; Yogapriya J

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1402-1412

In last decades online product sale is increased. The customers want to buy a quality product is very difficult in recent year. After buying only we know the problems in the product. After lancing many months users buying the product with problems. But many users put their Opinion in the review pages. Customers are very difficult to find the best product. Opinion Mining (OM) is the best tool for selecting the best product. OM on Product reviews refers to the process of analyzing the sentiment associated with it. This paper discussed about an attribute – level sentiment analysis of the product was done and also performs a three – class classification

AN IMPROVED RANDOM FOREST APPROACH FOR PREDICTING TUBERCULOSIS

R. Beaulah Jeyavathana; Kalpana G; K.V. Kanimozhi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1766-1771

Tuberculosis is one of the perilous infectious diseases that can be categorized by the evolution of tubercles in the tissues. This disease mainly affects the lungs and also the other parts of our body. Five stages are being used to detect tuberculosis disease. They are pre-processing an image, segmenting the lungs and Extracting the feature, Feature Selection and Classification. The optimal features are selected by Modified Random forest. Finally, Support Vector Machine classifier method is used for image classification. The proposed system accuracy results are better than the existing method inclassification.

Plant Curl Disease Detection And Classification Using Active Contour And Fourier Descriptor

M. Bala Naga Bhushanamu; M. Purnachandra Rao; K. Samatha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 5, Pages 1088-1105

Automatic plant leaf curl detection is an important step towards the development of Computer-aided crop damage analysis systems. It helps in analyzing the health condition of the plants through leaf images. Image processing techniques are recently being used to analyze the condition of the leaf and identify the disease that inflicted the crop. Leaf curl disease can be identified by analyzing the edges of the leaf. This paper presents a procedure to identify the curl disease occurring in plant leaves using active contour, Fourier feature descriptor, and deep learning. Active contour is used to identify the shape of the leaf. The edge contour of the leaf is then given to the Fourier feature descriptor. The feature extracted using the Fourier descriptor is invariant to the angle and size of the leaf. The same feature vector is produced in any given angle and size of the leaf in the image. The features are trained using 1D CNN. The model can then be used to classify new images and automatically identify the leaf have curl disease or not. The experimental results prove that the proposed algorithm produces good results in identifying the leaf curl disease.

Brain Tumor And Intracranial Haemorrhage Feature Extraction And Classification Using Conventional And Deep Learning Methods

R. Aruna Kirithika; S. Sathiya; M. Balasubramanian; P. Sivaraj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 237-258

Presently, brain tumor (BT) and Intracranial hemorrhage (ICH) detection and classification processes become essential to save human lives. Automated diagnosis model using deep learning (DL) models finds useful to attain improved diagnostic outcome. This paper presents an ensemble of handcrafted and deep features for BT and ICH diagnosis. The proposed model comprises of three important processes, such as preprocessing, feature extraction and classification. The preprocessing of the input image takes place in three ways namely skull stripping, bilateral filtering (BF) and contrast limited adaptive histogram equalization (CLAHE) based contrast enhancement. In addition, scale invariant feature transform (SIFT) and AlexNet models are used for feature extraction process. In order to classify the existence of BT and ICH, two classification models is carried out such as gaussian naïve bayes (GNB) and random forest (RF).For validating the effective diagnostic performance of the proposed model, a set of simulations were carried out to determine the different class labels. The simulation outcome indicated the effective performance with the maximum sensitivity of 92.41%, specificity of 100%, and accuracy of 94.26%.