A Hybrid Deep Learning Approach for Texture Analysis

Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of experts provides stability in classification rates among different datasets

A compressive survey on different image processing techniques to identify the brain tumor.

Medical imaging technology has revolutionized health care over the past three decades, allowing doctors to detect, cure and improve patient outcomes. Medicinal imaging involves pictures - of internal organs, parts, tissues and bones - for therapeutic examination and research purposes. X-ray and CT scanners are the two greatest results of progress in imaging methods supplanting 2D procedures. Magnetic resonance imaging (MRI) is an imaging procedure that is utilized in radiology to visualize interior structures of the body and better understand how they work. X-ray provides a 3D image of the body's interior; as well as being critical for tumor discovery, this also enables surgeons to more easily dissect infections or tumors than was possible with older X-beam technology, which provided a 2D image. This paper provides an overview of different systems that can be used for distinguishing and preparing medical images

AUTOMATED BRAIN TUMOR SEGMENTATION IN MR IMAGES USING A HIDDEN MARKOV CLASSIFIER FRAMEWORK TRAINED BY SVD-DERIVED FEATURES

Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images

A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images

In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing.  This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here.  The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.

A hybrid deep learning approach for texture analysis

Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of classifiers shows the stability of classification among different datasets and slight improvement compared to state of the art methods. The classifiers are fused using confusion matrix after independent training of each using the same training set, then put to test. Statistical information about each classifier is fed to a confusion matrix that generates two confidence measures used in building two binary classifiers. The binary classifier is allowed to activate or deactivate a classifier during testing time based on a confidence measure obtained from the confusion matrix. The method obtained results approaching state of the art with a difference less than 1% in classification success rates. Moreover, the method was able to maintain this success rate among different datasets while other methods had failed to obtain similar stability. Two datasets had been used in this research Brodatz and Kylberg where the results came 98.17% and 99.70%. In comparison to conventional methods in the literature, it came as 98.9% and 99.64% respectively

Разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках компьютерного томографа

Цель работы – разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках КТ. В процессе исследования были проанализированы существующие алгоритмы обнаружения образований в легких человека на снимках КТ. В результате исследования был разработан алгоритм для обнаружения образований в легких на снимках КТ, инвариантный к типу образования в легких, а также программные средства, позволившие оценить качество предложенного метода. Область применения: диагностика заболеваний легких человека, сбор статистической информации о видах заболеваний легких. Значимость работы заключается в практической разработке универсального метода для обнаружения образований легких человека – алгоритм показал высокое качество классификации независимо от типа образования.Objective - development of algorithms and program application for lungs nodules detection on CT scans. In the study, common methods and approaches for lungs nodules detection on CT scans have been observed. The results of the study are the developed algorithm for the detection of lungs nodules on CT scans, which is invariant to the type of formations in the lungs, and the implemented software tools that allows evaluating the quality of the proposed method. Scope: diagnostics of human lung diseases, collection of statistical information about the kinds of lung diseases. The significance of the work is the practical development of a universal method for the detection of human lung formations that has shown a high quality of classification, regardless of formation type in the lungs

Разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках компьютерного томографа

Цель работы – разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках КТ. В процессе исследования были проанализированы существующие алгоритмы обнаружения образований в легких человека на снимках КТ. В результате исследования был разработан алгоритм для обнаружения образований в легких на снимках КТ, инвариантный к типу образования в легких, а также программные средства, позволившие оценить качество предложенного метода. Область применения: диагностика заболеваний легких человека, сбор статистической информации о видах заболеваний легких. Значимость работы заключается в практической разработке универсального метода для обнаружения образований легких человека – алгоритм показал высокое качество классификации независимо от типа образования.Objective - development of algorithms and program application for lungs nodules detection on CT scans. In the study, common methods and approaches for lungs nodules detection on CT scans have been observed. The results of the study are the developed algorithm for the detection of lungs nodules on CT scans, which is invariant to the type of formations in the lungs, and the implemented software tools that allows evaluating the quality of the proposed method. Scope: diagnostics of human lung diseases, collection of statistical information about the kinds of lung diseases. The significance of the work is the practical development of a universal method for the detection of human lung formations that has shown a high quality of classification, regardless of formation type in the lungs