Online ISSN: 2515-8260

Keywords : Feature extraction

Cascaded CNN with Haar Wavelet Feature based Brain Tumor Detection Technique

G. Dheepa1, S. Uma Shankari

European Journal of Molecular & Clinical Medicine, 2022, Volume 9, Issue 7, Pages 8395-8405

Abnormal tumor image identification from brain Magnetic Resonance Images (MRI) is essential for medical diagnostics. In this research, Cascaded Convolutional Neural Network (CCNN) with Haar wavelet features based brain tumor detection technique has been proposed for automatic identification of tumor images. The significant LL sub-band features are first extracted in all image slices. These slices are further processed using CCNN architecture for brain tumor detection. In this architecture, each image slice is convolved with three different 7 x 7, 3 x 3 and 5 x 5 kernels to produce three separate feature maps. These feature maps are cascaded to be processed into the hierarchy of convolutional, pooling and softmax layers to predict whether an image is having a tumor or not. This proposed algorithm is implemented using the BRATS-2018 training dataset. It achieves 96% of Accuracy, 97 % of F1-score, 97 % of Precision, 97 % of Specificity and 96 % of Sensitivity values.

Integrated Deep Learning Model with Hybrid Texture based Me di c a l Image Retrieval System

Dr. A. Jayachandran, Dr.G.Shanmugarathinam

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 1, Pages 2408-2418

Electronic restorative imaging and examination techniques utilizing different modalities have
encouraged early determination. The development of the computer-aided retrivel systems in
recent years turned them into a nondestructive and popular method for diagnosis the disease
in medical images. In this work, adaptive Gabor wavelet filter bank and Texton based a
feature descriptor is developed for medical image retrieval. The design of the proposed
descriptor basis provides flexibility in order to extract the dominant directional features from
medical images.. Also, we present a novel end-to-end integrated deep learning model using
Convolutional Neural Network (CNN) and the Long Short-Term Memory cell (LSTM). The
proposed integrate deep learning descriptor is compared to other descriptor such as CCM,
CHD, MTH and MSD using the datasets such as New Caltech , Corel-1000,Oliva and Corel-


J Sujithra; M Ferni Ukrit

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1168-1183

Almost all over the world, the economy mainly depends on the production of food.
Computer vision technology plays a pivotal role in the field of agriculture. The dream of this
research is to produce successful crops in the agricultural sector. Successful farming can
increase crop production in terms of both quality and quantity. The farming performs eight
major phases which begin from crop selection to harvesting. At any of these phases, the
disease and pest may destroy plants. However, the leaves are found to be the most damaged
part in disease identification. A lot of articles are taken out for the survey that endorses the
mechanism of image processing and deep learning for the detection and classification of
diseases from the crop leaves. This survey provides an overview of the pros and cons of all
such articles on various research aspects. The effectiveness of state-of-the-art methods is
explored to identify the techniques that seem to work well across different crops. This paper
indicates that algorithms like Support Vector Machine and Neural Network play an important
role in the crop disease identification and classification.

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%.

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.

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

A Literature Review on Detection of Plant Diseases

Prof. A. R. Bhagat Patil; Lokesh Sharma; Nishant Aochar; Rajat Gaidhane; Vikas Sawarkar; Dr Punit Fulzele; Dr. Gaurav Mishra

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 1605-1614

With increase in population the need for food is on rise, in such circumstances, plant diseases prove to be a major threat to agricultural produce and result in disastrous consequences for farmers. Early detection of plant disease can help in ensuring food security and controlling financial losses. The images of diseased plants can be used to identify the diseases. Classification abilities of Convolutional Neural Networks are used to obtain reliable output. Google’s pretrained model ‘Inception v3’ is used. The Inception v3 model is trained over a dataset of diseased plants obtained from ‘Plant Village Dataset’. The developed detection approach is evaluated on measures of F1 score, precision and recall.

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.


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.