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We develop and study an automated method for the detection and classification of three types of technological defects in rolled metal products. The method is based on the ResNet50 neural network classifier, which makes it possible to recognize three classes of defects in the images of flat surfaces with a total accuracy of 95.8% on the basis of the analysis of experimental data with unbalanced numbers of images of different types. To train the classifier, we used about 88,000 images. It is shown that the application of the model developed on the basis of the ResNet50 neural network guarantees its excellent productivity, high quality of recognition, high speed, and high accuracy, which turns the proposed classifier into an effective tool for the solution of problems of engineering diagnostics and nondestructive testing aimed at the classification of defects on the surfaces of rolled metal products.

BACKGROUD AND OBJECTIVE: The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. METHODS: In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. RESULTS: The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. CONCLUSIONS: The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given...

The paper shows the efficiency of the multi-class recognition of technical states the welded joint tank by using the neural network classifier that based on Probabilistic Neural Network. Implementation of modern multichannel monitoring systems for determination the technical state of spatial objects requires researching of changes of a stress-strain state construction elements which function under the operation pressure and possible impact its structural integrity. Such researching are needed for prevention of cracks or damage determining and future prediction of object’s technical state. In the paper the tasks of a multi-class recognition of a state of multi-site damage the welded joint tank are determined. In the research was created the model of the object with probable location places cracks. One of them is a vertical crack and two are horizontal. Directions of their propagation are given. Cracks have the same value. The first researching included when cracks turn up one by one. The next one related with multi-focal defects when cracks were arised and they evolve in parallel and independently. The data of strain in the structural of the welded joint tank where sensors were attached is given. The research of the possibility of the error-free recognition was conducted by the developed classifier based on the stress-strain state of the geometric model of the tank structural elements with multi-site damage, where sensors are located. The development of the classifier was done by using Probabilistic Neural Network, which provides the best results of a multi-class recognition for determination technical state of spatial object with multidimensional vectors of diagnostic features. As a result, the probability of recognition from the network influence parameter, which shows the effectiveness of the neural network classifier for localization of single damage and localization of multiple cracks, was established

Background: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. Methods: The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. Results: The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. Conclusion: This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders’ productivity and improve the final outcome accuracy of the screening process.

Computer-assisted colon cancer detection on the histopathological images has become a tedious task due to its shape characteristics and other biological properties. The images acquired through the histopathological microscope may vary in magnifications for better visibility. This may change the morphological properties and hence an automated magnification independent colon cancer detection system is essential. The manual diagnosis of colon biopsy images is subjective, sluggish, laborious leading to nonconformity between histopathologists due to visual evaluation at various microscopic magnifications. Automatic detection of colon across image magnifications is challenging due to many aspects like tailored segmentation and varying features. This demands techniques that take advantage of the textural, color, and geometric properties of colon tissue. This work exhibits a segmentation approach based on the morphological features derived from the segmented region. Gabor Wavelet, Harris Corner, and DWT-LBP coefficients are extracted as it should not be dependent on the spatial domain with respect to the magnification. These features are fed to the Genetically Optimized Neural Network classifier to classify them as normal and malignant ones. Here, the genetic algorithm is used to learn the best hyper-parameters for a neural network.

Summary Neural networks have shown strong potential in research and in healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where more data are typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately 100 images per condition to classify 6 different genetic conditions. We analyzed both preprocessed images that were cropped to show only the skin lesions as well as more complex images showing features such as the entire body segment, the person, and/or the background. The classifier construction process included attribution methods to visualize which pixels were most important for computer-based classification. Our classifier was significantly more accurate than pediatricians or medical geneticists for both types of images and suggests steps for further research involving clinical scenarios and other applications., We built a neural network classifier for images of skin findings in selected genetic conditions. We compared the accuracy (using both cropped and larger images) of our classifier to that of pediatricians and medical geneticists. We show how attribution methods can help refine a classifier.

In this study, a novel method, fuzzy nearest neighbor partitioning (FNNP), is proposed for improving the classification capability of the neural network. For the original nearest neighbor partitioning method, the “short-sightedness defect” problem restrains the nearest neighbor partitioning method to learn valuable experience from the distribution information outside the samples’ nearest neighbors. Moreover, the noise sensitive problem that exists in the process of classifying the unknown samples further influences the performance of the nearest neighbor partitioning method. To overcome the short-sightedness defect problem, fuzzy logic theory is integrated into the FNNP, and promotes the ability that the FNNP learns experience from extensive distribution information. In addition, an improved classification strategy, which adopts the concept of the fuzzy nearest neighbor, is employed to increase the immunity of FNNP to noise when the unknown samples are classified. The proposed FNNP is compared with other classification methods for some famous datasets. The results of the experiments indicate that the proposed method achieves promising classification performance for various indicators.

Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8%. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99% was obtained for full training CNN.

Traditional training algorithms in artificial neural networks (ANNs) show some inherent weaknesses, such as the possibility of falling into local optimum, slow learning speed, and the inability to determine the optimal neuronal structure. To remedy the deficiencies of traditional neural networks, this paper proposes a novel neural network classifier (NNC) using the beetle antennae search (BAS) algorithm, termed BASNNC. The BAS algorithm is explored to optimize the weights of the NNC. The network of the proposed BASNNC adopts three-layer structure, including an input layer, a hidden layer, and an output layer. Quite differing from the traditional training algorithm using a principle of gradient descent, the weights between the hidden and output layers are optimized by the BAS algorithm, which effectively improves the computational speed of the classifier. The numerical studies, applications to pattern classification and comparisons with an error back-propagation neural network model, show that the proposed BASNNC has faster computational speed and higher classification accuracy.

© 2013 IEEE. In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.

One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify persons based on some physiological or behavioral characteristics that they own. Photoplethysmography (PPG) is a physiological signal that is used to describe the volumetric change of blood flow in peripherals with heartbeats. The PPG signals gained some interest of researchers in the last few years, because they are used non-invasively, and they are easily captured by the emerging IoT sensors from fingertips. This paper presents a PPG-based approach to identify persons using a neural network classifier. Firstly, PPG signals are captured from a number of persons using IoT sensors. Then, unique features are extracted from captured PPG signals by estimating the Mel-Frequency Cepstral Coefficients (MFCCs). These features are fed into an Artificial Neural Network (ANN) to be trained first and used for identification of persons. A dataset of PPG signals for 35 healthy persons was collected to test the performance of the proposed approach. Experimental results demonstrate 100% and 98.07% accuracy levels using the hold-out method and the 10-fold cross-validation method, respectively.

This paper presents an enhanced pattern recognition neural network classifier for ground target classification in FSR. The hybrid enhanced model is based on a different combination of pre-processing techniques. Wavelet and feature extraction techniques were applied on time-domain raw data of three vehicles before it is processed by the neural network classifier. Several scenarios were undertaken to study and analyze the effect of adding the proposed techniques to the NN classifier’s performance. The result reveals the potential and effectiveness of pre-processing to increase the performance by 79.2% to previous work, which gives more than 97% true classification.

A method for disturbance positioning in noisy environment is introduced. By the partitioning algorithm and a well-trained B-P neural network classifier, an average locating accuracy of 95% was demonstrated in the field test.

In this study we present a system for automated processing of signals from the polysomnogram (PSG) for the detection of apnoea and non-apnoea arousals. The PSG signals were divided into 15 second epochs and 59 time- and frequency-domain features were derived for each epoch. Features from adjacent 4 epochs were combined and processed with a bank of ten feed-forward neural networks each with a single hidden layer of 20 units. The system outputs a 200 Hz annotation signal containing probability estimates that each sample was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using three class and two class metrics. With the system classifying three classes, the volume under the receiver operator characteristic (ROC) surface was 0.75 with an optimal specificity of 0.72, a sensitivity of 0.76 for the apnoea arousals, and a sensitivity of 0.69 for the non-apnoea arousals. When the two arousal classes were combined into one arousal class, the area under the precision recall curve was 0.74, the area under the ROC curve was 0.91, with an optimal specificity and sensitivity of 0.85.

Error-Related Potential (Errp) is the bioelectric potential elicited in human brain as a result of the cognitive state of awareness when an error is perceived. Identifying ErrP from a single trial electroencephalogram (EEG) data can be used in taking corrective actions to fix the error or as a learning strategy in Brain Computer Interface (BCI) systems. The ErrP dataset recorded using EEG will contain both erroneous and correct actions. A classifier such as the Artificial Neural Network (ANN) can be trained to identify the erroneous versus correct action from a single trial EEG data. However, the classifier will have large number of parameters to be learned, and typically, the ErrP dataset is unbalanced with smaller number of erroneous trials. Therefore, the trained classifier may not generalize the data well. To classify the ErrP with better accuracy, an ANN architecture is proposed in this work. Learning the parameters of the ANN is carried out in two stages (Stage-1 and Stage-2) in the proposed method. The first stage of learning will have relatively large feature samples collected from several subjects. The first stage learning is aimed to capture the global characteristics of the ErrP. In the second stage, the pre-trained ANN classifier from the first stage is tuned for each subject. The ErrP dataset has two sessions dataset recorded from six subjects and the Stage-1 and Stage-2 training models are cross-validated. The overall classification accuracy achieved after cross-validation is 74.78 ± 3.43% and 86.03 ± 1.02% for erroneous and correct trials respectively. The improvement in the classification accuracy achieved is 12.67% and 15.51% for erroneous and correct trials respectively compared with the existing statistical classifier method. The method proposed shows an efficient way to train ANN classifier to achieve higher classification accuracy for unbalanced and smaller dataset such as ErrP.

The information about intended hand gestures can be extracted by processing surface electromyography signals using non-invasive commercial off the shelf surface electromyography data acquisition devices. Surface electromyography signals have a great potential for use in multi-functional prosthetic controllers. The objective of this study is the implementation of a classifier that can be used to classify gestures from Myo-electric data obtained from the Myo-armband. This study describes in detail a method for data acquisition, feature extraction, and offline gesture classification using Artificial Neural Network. The performance is then compared with a Support Vector Machine Classifier. The proposed approach results in an accuracy greater than 94% for validation data set for classification of five distinct hand gestures. It could be concluded that this technique could be used in the human-machine interfaces with five distinct control signals including rest. A significant observation in this study was that a single artificial neural network taking inputs from all sensors simultaneously gives inferences with better accuracy compared to a system with a separate neural network for each sensor with a majority voting to decide the classification of the gesture.

Feature extraction is one of the key technologies in pattern recognition, which has great influence on the accuracy of text recognition. In this paper, The Tai Le are extracted with coarse grid features, directional line element feature and penetration number feature, evaluated using BP neural network classifier. The results show that the direction line element feature on the Tai Le print recognition data set YDH2019.1 has higher recognition accuracy than the other two features, is more suitable for the Tai Le recognition.

This paper presents a neural network classifier approach to detecting precise within-document (WD) and cross-document (CD) event coreference clusters effectively using only event mention based features. Our approach does not rely on any event argument features such as semantic roles or spatio-temporal arguments and uses no sophisticated clustering approach. Experimental results on the ECB+dataset show that our simple approach out performsstate-of-the-art methods for both within-document and cross-document event coreference resolution while producing clusters of high precision, which is useful for several downstream tasks.

In the industries of wood furniture and wood floor manufacturing, the color and texture of wood panel are important surface feature parameters. At present, the comprehensive measurement visual feature above-mentioned of wood panel mainly relies on manual visual inspection, which is greatly influenced by human factors, slow in measurement speed and low in accuracy. In this paper, color histogram and gray level co-occurrence matrix are used to digitize the visual features of wood panel, and a parameter system suitable for describing the visual features of wood panel is established. Using by the visual feature parameters system Vnew of wood panel, the recognition rate of BP neural network classifier is up to 95.25%, and the results show that the proposed method is feasible.

Ventricular tachycardia is a life threatening medical emergency. Discerning dangerous ventricular rhythms with safe Sinus tachycardia based on heart rate is very tough as they are having similar heart rate. Most of the existing research used time information for classification which may lead false alarm. Hence a CMOS circuit is proposed to classify ventricular-tachycardia based on morphological changes in QRS complex. The design includes sample and hold circuit for sampling QRS complex, mapping circuit for map the given input signal to unit length, hamming neural network and winner take all circuits for classification of ventricular tachycardia. This design is implemented using 180nm CMOS technology with the operating voltage and power consumption of 19.81µW.

This paper is concerned with the combination of microscopic image processing tools and artificial intelligence in order to detect and recognize the Nosema disease, which affects honey bees. In fact, the use of imaging processing tools in medical and biological sciences was significantly increased, especially in the detection and diagnosis of diseases. Our work focuses on a segmentation method which analyses the objects of an image and decides if it is Nosema or not. A set of the significant values of geometric and texture features has been calculated and fused as a definition for a Nosema cell. A MultiLayer neural network was applied as classifier to detect and recognize this disease on Nosema images. Finally, our automatic segmentation approach shows a 91% of accuracy, in the process of identifying the Nosema disease.

Researchers are continuously making breakthroughs on the impact of deep learning in the medical industry. This approach of Deep Learning (DL) in neuroimaging creates new insights in modification of brain structures during various disorders, helping capture complex relationships that may not have been visible otherwise. The main aim of proposed work is to effectively detect the presence of schizophrenia, a mental disorder that has drastic implications and is hard to spot, using Magnetic Resonance Image Features from fMRI Database. Dataset is then fed to a Neural Network classifier, which learns to predict and give indications for preventing the onset of Schizophrenia using regression models.

Presently, the usage of medicinal images has drastically increased and it provides extensive details related to the patient’s health status. It shows the applicability of diagnosing the disease and stored in a memory for examination purposes. For the retrieval of medical images in a real world environment, significant need is present in the designing of an effective medical image indexing and retrieval technique. This paper offers an efficient medical image retrieval (MIR) model through feature extraction based classification model. Here, Directional local ternary quantized extrema patterns (DLTerQEPs) and autocorrelogram (AC) based feature extraction process takes place to extract texture and color features. Next, neural network (NN) based classification process takes place. The investigation of the simulation results takes place to showcase the betterment of the presented mode. During experimentation process, it is noticed that the presented model is superior to compared methods.

Classification is a machine learning technique that is used to find the membership of an object to a given set of classes or groups. Neural network (NN) classifier always suffers some issues while examining outliers and data points from nearby classes. In this paper, we present a new hyper-credal neural network classifier based on belief theory. This method is based on credal classification technique introduced in Dempster-Shafer Theory (DST). It allows a data point to belong not only to a specific class but also to a meta-class or an ignorant class based on its mass. The sample which lies in an overlapping region is accurately classified in this method to a meta-class which corresponds to the union of the overlapping classes. Therefore this approach reduces the classification error at the price of precision. But this decrease in precision is acceptable in applications such as medical, defence related applications where a wrong decision would cost more than avoiding some correct decisions. The results and analysis of different databases are given to illustrate the potential of this approach. This idea of hyper-credal classification is extended to convolutional neural network (CNN) classifiers also.

Alcoholism involves psychological and biological components where multiple risk factors come into play. Assessment of the psychiatric emergency is a challenging issue for clinicians working with alcohol-dependent patients. Identifying alcoholics from healthy controls from their EEG signals can be effective in this scenario. In this research, we have applied two instance-based classifiers and three neural network classifier to classify Electroencephalogram data of alcoholics and normal person. For data preprocessing, we have applied discrete wavelet transform, Principal component analysis and Independent component analysis. After successful implementation of the classifiers, an accuracy of 95% is received with Bidirectional Long Short-Term Memory. Finally, comparing the performance of the two categories of algorithms, we have found that neural networks have higher potentiality against instance-based classifiers in the classification of EEG signals of alcoholics.

Objective: The aim was to study image fusion-based cancer classification models used to diagnose cancer and assess medical problems in earlier stages that help doctors or health care professionals to make the treatment plan accordingly. Methods : In this work, a novel image fusion method based on Curvelet transform is developed. CT and PET scan images of benign type tumors were fused together using the proposed fusion algorithm and the same way, MRI and PET scan images of malignant type tumors were fused together to achieve the combined benefits of individual imaging techniques. Then, the marker-controlled watershed algorithm was applied on fused images to segment cancer affected area. The various color features, shape features and texture-based features were extracted from the segmented image. Following this, a data set was formed with various features, given as input to different classifiers namely neural network classifier, Random forest classifier, and K-NN classifier to determine the nature of cancer. The results of the classifier showed normal, benign or malignant category of cancer. Results: The performance of the proposed fusion algorithm was compared with the existing fusion techniques based on the parameters PSNR, SSIM, Entropy, Mean and Standard Deviation. Curvelet transform based fusion method performs better than already existing methods in terms of five parameters. The performances of the classifiers were evaluated using three parameters: accuracy, sensitivity, and specificity. The K-NN Classifier performed better compared to the other two classifiers and it provided an overall accuracy of 94%, sensitivity of 88% and specificity of 84%. Conclusion: The proposed Curvelet transform based image fusion method combined with the KNN classifier provides better results compared to other two classifiers when two input images were used individually.

In the medical image analysis Classification plays a crucial role especially in disease diagnosis. It helps physicians and radiologists in their decision interpretations about the disease. Tuberculosis (TB) is an infectious disease arised by Mycobacterium tuberculosis. Lot of researchers accomplished work in TB diagnosis. Still TB detection is a key problem. Accordingly an accurate TB detection process is suggested using optimal neural network classifier. In our work Initially the feature vector will be drawn out from the given dataset by computing First order statistical, structural and gray level dependent features for all input images present in the database. Later the feature selection process will be done. On that chosen features can be executed to train the Neural Network classifier. In the proposed system, we use center based genetic algorithm (CBGA) in order to optimize the weights of Network. It classifies the given image as abnormal or normal. The proposed work is implemented using the MATLAB. Compared to existed methods, performance of our projected system is enhanced in respect of Sensitivity, Specificity and Accuracy.

Fighter aircraft recognition is important in military applications to make strategic decisions. The complexity lies in correctly identifying the unknown aircraft irrespective of its orientations. The work reported here is a research initiative in this regard. The database used here was obtained by using rapid prototyped physical models of four classes of fighter aircraft: P51 Mustang, G1-Fokker, MiG25-F, and Mirage 2000. The image database was divided into the training set and test set. Two feature sets, Feature Set1 (FS1) and FS2, were extracted for the images. FS1 consisted of 15 general features and FS2 consisted of 14 invariant moment features. Four multilayered feedforward backpropagation neural networks were designed and trained optimally with the normalized feature sets. The neural networks were configured to classify the test aircraft image. An overall accuracy of recognition of 91% and a response time of 3 s were achieved for the developed automatic fighter aircraft model image recognition system.

In recent years, the detection of a human face from the video has become an interesting research topic due to the video surveillance and other security issues. Efficient face detection from the video has become an immense need as it can provide various identity measures in the field of defense and other security-related areas. In our proposed method we have developed an efficient method of face detection to index a particular face from different video shots. The proposed method can be divided into Different modules. In the first module, human face from the video is extracted using segmentation technique. In our proposed method, we have used Kernel-based Possibilistic C-Means for segmentation purpose. The second module in our method is the feature extraction process where shape, LBP, and some geometrical features are extracted. The various shape features like area, circularity, and eccentricity are extracted. Once the feature values are extracted we track the particular face using forward tracking process. After the tracking process, we employ the classification technique. The classifier we utilized here is the improved neural network where the weights factors are optimized using the modified cuckoo search algorithm. The performance is compared with some existing works in order to prove the efficiency of our proposed method.

The nearest neighbor partitioning (NNP) method is a high performance approach which is used for improving traditional neural network classifiers. However, the construction process of NNP model is very time-consuming, particularly for large data sets, thus limiting its range of application. In this study, a parallel NNP method is proposed to accelerate NNP based on Compute Unified Device Architecture(CUDA). In this method, blocks and threads are used to evaluate potential neural networks and to perform parallel subtasks, respectively. Experimental results manifest that the proposed parallel method improves performance of NNP neural network classifier. Furthermore, the application of parallel NNP in performance evaluation of cement microstructure indicates that the proposed approach has favorable performance.

At present, the intelligent systems are used to study the functional state of living objects. They are often built on a multimodal basis, i.e. decision-making is carried out through several information channels with the subsequent aggregation of these decisions. In the proposed method, the electrocardiosignal is a source of information. The principle of multimodality is implemented by analyzing of various methods and aggregating the results using neural networks of block and hierarchical structure. The method was tested on the classification of patients with pneumonia with a clear diagnosis. A group of volunteers without pulmonary pathologies was used as an indifferent class.

Diabetic retinopathy (DR) is one of the main prevalent diabetes problems, causing blurry vision and degeneration amongst adults of working age. The first symptoms of DR include Microaneurysms (MA). A Genetic Algorithm-Artificial Neural Network (GA-NN) technique is developed for early diagnosis of DR. There are five steps of the proposed framework. Image pre-processing is achieved using r-polynomial transformation. In the extraction, the K-means algorithm is used to segment blood vessels, and candidate patches were generated. Shape attributes, GLCM and LBP features have been derived from excluded blood vessel image and from patches separately. To achieve independent classification, GA-NN classifiers is employed. The ultimate decision system projects the MA or non-MA class labels by plurality voting for eachclassifier. This methodology was tested on two databases: e-Ophtha-MA and DIARETDB1. The e-ophtha-MA and DIARETDB1 datasets had AUCs of 0.89 and 0.87, respectively, on the receiver operating characteristic (ROC) curve.

The present work proposes a classification framework for the prediction of breast density using an ensemble of neural network classifiers. Expert radiologists, visualize the textural characteristics of center region of a breast to distinguish between different breast density classes. Accordingly, ROIs of fixed size are cropped from the center location of the breast tissue and GLCM mean features are computed for each ROI by varying inter-pixel distance ‘d’ from 1 to 15. The proposed classification framework consists of two stages, (a) first stage: this stage consists of a single 4-class neural network classifier NN0 (B-I/B-II/B-III/B-IV) which yields the output probability vector [PB-I PB-II PB-III PB-IV] indicating the probability values with which a test ROI belongs to a particular breast density class. (b) second stage: this stage consists of an ensemble of six binary neural network classifiers NN1 (B-I/B-II), NN2 (B-I/B-III), NN3 (B-I/B-IV), NN4 (B-II/B-III), NN5 (B-II/B-IV) and NN6 (B-III/B-IV). The output of the first stage of the classification framework, i.e. output on NN0 is used to obtain the two most probable classes for a test ROI. In the second stage this test ROI is passed through one of the binary neural networks, i.e. NN1 to NN6 corresponding to the two most probable classes predicted by NN0. After passing the entire test ROIs through the second stage, the overall accuracy increases from 79.5% to 90.8%. The promising results achieved by the proposed classification framework indicate that it can be used in clinical environment for differentiation between breast density patterns.

This paper inspects a structure for search-based-face annotation using mining weakly labeled web facial images. Facial photographs are candidly present on the Internet, from that a few facial photographs are properly labeled, however, some of them are not correctly labeled. These facial photographs are repeatedly incomplete and noisy. For enhancing tag quality of weakly net facial photographs, ULR approach is also advantageous for cleansing or filtering the tags of net facial photographs (Wang et al. in IEEE Trans Knowl Data Eng 26, [1]). Big headache issue for search-based face annotation scheme is, whenever the given test facial portrait is not a common person, there are no much more same facial photographs present on the web. A supervised appropriate name tag can be given to a test face portrait by employing face annotation using search-based paradigm, but it also increases the efficiency and scalability. The supervised neural network classifier approach is looking to optimize the tag quality of face portrait by majority voting against the face annotation by search-based paradigm.