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Feature extraction and classification plays a crucial role in the automated analysis of breast ultrasound (BUS) images. Due to varying sonographic characteristics of benign and malignant lesions, t...

Ultrasound imaging is most popular technique used for breast cancer screening. Lesion segmentation is challenging step in characterization of breast ultrasound (US) based Computer Aided Diagnosis (CAD) systems due to presence of speckle noise, shadowing effect etc. The aim of this study is to develop an automatic lesion segmentation technique in breast US with high accuracy even in presence of noises, artifacts and multiple lesions. This article presents a novel clustering method called Multi-scale Gaussian Kernel induced Fuzzy C-means (MsGKFCM) for segmentation of lesions in automatically extracted Region of Interest (ROI) in US to delimit the border of the mass. Further, a hybrid approach using MsGKFCM and Multi-scale Vector Field Convolution (MsVFC) is proposed to obtain an accurate lesion margin in breast US images. Initially, the images are filtered using speckle reducing anisotropic diffusion (SRAD) technique. Subsequently, MsGKFCM is applied on filtered images to segment the mass and detect an appropriate cluster center. The detected cluster center is further used by MsVFC to determine the accurate lesion margin. The proposed technique is evaluated on 127 US images using measures such as Jaccard Index, Dice similarity, Shape similarity, Hausdroff difference, Area difference, Accuracy, F-measure and analysis of variance (ANOVA) test. The empirical results suggest that the proposed approach can be used as an expert system to assist medical professionals by providing objective evidences in breast lesion detection. Results obtained are so far looking promising and effective in comparison to state-of-the-art algorithms.

Hyperspectral band selection (BS) is an intrinsic problem in hyperspectral image processing. This paper proposes a novel hyperspectral BS method based upon mutual information (MI). The proposed method tries to find the least redundant and most informative band subset from the whole image data cube iteratively. The informative and redundancy qualities of a band are measured by its MI with the set of unselected bands and the set of selected bands. Experimental findings on two different datasets reveal that the presented method can attain comparable performance for the classification of the hyperspectral image dataset compared to the other ranking based band selection method.

Land cover change detection has been a subject of dynamic inquiry among the remote sensing group. On account of the tremendous data accessible from satellites, it has been attracting the data mining group to explore novel techniques to discover the land cover change. The Terra Moderate Resolution Imaging Spectrometer (MODIS) Vegetation Index (i.e., EVI/NDVI) data products are employed for land cover change detection. In order to address and in-depth understanding about the data, this chapter presents the detail description about vegetation-related data products and quality assurance bits. These vegetation-related data products are accompanied with several confrontations such as seasonality of data, spatial and temporal resolution, high dimensional, missing values, and poor-quality measurements. The researcher discusses and proposes the few methods to address the most of the challenges and prepare EVI time series data set such that the resulting time series data set is available with high quality and noise free. We hope this chapter provides new direction to data mining researchers and/or time series analyst to determine the land cover change detection, classification, and study of spatio-temporal data.

This paper presents a qualitative analysis of the Recursive Search Algorithm (RSA) technique. The RSA is a land cover change detection technique, which determines that a particular land cover is changed or not. It is a data mining approach and established on the segmentation technique. Furthermore, the RSA is a land cover change detection technique and identifies the land cover change by finding the sudden gains and drop-offs in a vegetation index time series data. The proficiency of the RSA technique is exhibited by using qualitative analysis. To exhibit the qualitative analysis, the RSA method is applied to the Naya Raipur Area data set, which is an enhanced vegetation index time series data set. It has been noticed that the significant land cover changes have been happening in the Naya Raipur Area.

Chest X-ray (CXR) is the most popular imaging modality used for preliminary diagnosis of pulmonary diseases. In automatic computer-aided diagnosis (CAD), the number of false-positive cases can be reduced by segmenting out the normal anatomical structures. Demarcating lung parenchyma on CXR image is challenging due to complex anatomical structures of the human thoracic cavity. The extracted lungs boundary suffers from undesirable artifacts such as ridges and pits. This paper presents an algorithm to adaptively scan the inner lung boundary to correct the undesirable artifacts. Further, the algorithm is context-aware, it takes care of normal cavity due to the aortic knuckle and diaphragm borders. The algorithm is tested on 138 binary lung mask extracted from digital CXR images from Montgomery dataset. The quantitative, qualitative, statistical results reveal that the proposed algorithm outperforms the existing method. The average increase in segmentation accuracy is 2.561%.

Chest X-rays (CXR) are widely used radio-imaging modality for preliminary diagnosis of thoracic abnormalities. Automatic detection and localization of diseases will greatly enhance real-world diagnosis processes. The imprecise and subtle appearance of disease responses on chest radiographs makes it difficult to localize sometimes even for an expert radiologists. The radiographic pattern in the normal regions is different from the disease affected regions. The affected areas in the X-ray images exhibit a cloudy pattern that shows the symptoms of various diseases like tuberculosis, pneumonia, pleural effusion, etc. The clustering algorithms have the capability to form clusters based on extracted features from the image. In this paper, we have investigated the effectiveness of Fuzzy C-Means (FCM) and K-Means (KM) clustering techniques to localize the suspected abnormal regions in CXR images. The analysis is performed on small image patches extracted from the segmented lung fields, and the results are compared with ground truth data provided by the radiologist. The proposed system is validated using a publicly available Montgomery dataset. The results demonstrate the promising performance of the proposed technique in delimiting the suspected abnormal regions.

In text classification, the Global Filter-based Feature Selection Scheme (GFSS) selects the top-N ranked words as features. It discards the low ranked features from some classes either partially or completely. The low rank is usually due to varying occurrence of the words (terms) in the classes. The Latent Semantic Analysis (LSA) can be used to address this issue as it eliminates the redundant terms. It assigns an equal rank to the terms that represent similar concepts or meanings, e.g. four terms “carcinoma”, “sarcoma”, “melanoma”, and “cancer” represent a similar concept, i.e. “cancer”. Thus, any selected term by the algorithms from these four terms doesn’t affect the classifier performance. However, it does not guarantee that the selection of top-N LSA ranked terms by GFSS are the representative terms of each class. An Improved Global Feature Selection Scheme (IGFSS) solves this issue by selecting an equal number of representative terms from all the classes. However, it has two issues, first, it assigns the class label and membership of each term on the basis of an individual vote of the Odds Ratio (OR) method thereby limiting the decision making capability. Second, the ratio of selected terms is determined empirically by the IGFSS and a common ratio is applied to all the classes to assign the positive and negative membership of the terms. However, the ratio of positive and negative nature terms varies from one class to another and it may be very less for one class, whereas high for other classes. Thus, one common negative features ratio used by the IGFSS affects those classes of a dataset in which there is an imbalance between positive and negative nature words. To address these issues of IGFSS, a new Soft Voting Technique (SVT) is proposed to improve the performance of GFSS. There are two main contributions in this paper: (i) The weighted average score (Soft Vote) of three methods, viz. OR, Correlation Coefficient (CC), and GSS Coefficients (GSS) improves the numerical discrimination of words to identify there positive and negative membership to a class. (ii) A mathematical expression is incorporated in the IGFSS that computes a varying ratio of positive and negative memberships of the terms for each class. The membership is based on the occurrence of the terms in the classes. The proposed SVT is evaluated using four standard classifiers applied on five bench-marked datasets. The experimental results based on Macro_F1 and Micro_F1 measures show that SVT achieves a significant improvement in the performance of classifiers in comparison of standard methods.

Automated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi-scale Gaussian kernel induced fuzzy C-means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region-scalable fitting energy function. The result obtained from the MsGKFCM_S method is utilised to initialise the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters of the curve evolution process. The proposed approach is evaluated on a database of 127 breast US images consisting of 75 malignant and 52 solid benign cases. The performance of proposed approach is compared with other related techniques, using performance measures such as Jaccard Index, dice similarity, shape similarity, Hausdroff difference, area difference, accuracy and F-measure. Results indicate that the proposed approach can successfully detect lesions in breast US images, with high accuracy of 97.889 and 97.513%. Moreover, the proposed approach has the capability of handling shadowing effects, acoustic enhancement and multiple lesions.

The development of improved satellite technology generates a huge amount of remote sensing data, these data play the crucial role in natural resource management. The land use and land cover (LULC) change intensely affects local environment, as well as the global environment. Therefore, the quantifiable knowledge about LULC changes occur in global scale is important to make effective planning for conservation and precise use of natural resources, that has motivated the scientists to develop the various land cover change detection techniques. In this paper, we have proposed neural network-based approach, i.e., focused time delay neural network (FTDNN)-based approach for land cover change detection, which is a time series prediction-based approach and detect the sudden change in the enhanced vegetation index (EVI) time series. The performance of the proposed method has been addressed by using quantitative and qualitative analysis techniques. For the quantitative evaluation, the proposed algorithm is applied to the standard synthetic data set, which are analogous to EVI time series data set. The performance result of the proposed method compares with the four previously existing data mining-based benchmark techniques. The analysis was shown that the FTDNN-based method significantly outperforms than other techniques. For qualitative analysis, the San Francisco Bay Area data set has been used, which comprises real EVI time series. The proposed FTDNN-based method is applied to the San Francisco Bay Area data set and observe the interesting land cover changes. These outcomes indicate the effectiveness of data mining techniques for the land cover change detection problem.

Land cover change detection has been a topic of active research in the remote sensing community. Due to enormous amount of data available from satellites, it has attracted the attention of data mining researchers to search a new direction for solution. The Terra Moderate Resolution Imaging Spectrometer (MODIS) vegetation index (EVI/NDVI) data products are used for land cover change detection. These data products are associated with various challenges such as seasonality of data, spatio-temporal correlation, missing values, poor quality measurement, high resolution and high dimensional data. The land cover change detection has often been performed by comparing two or more satellite snapshot images acquired on different dates. The image comparison techniques have a number of limitations. The data mining technique addresses many challenges such as missing value and poor quality measurements present in the data set, by performing the pre-processing of data. Furthermore, the data mining approaches are capable of handling large data sets and also use some of the inherent characteristics of spatio-temporal data; hence, they can be applied to increasingly immense data set. This paper stretches in detail various data mining algorithms for land cover change detection and each algorithm’s advantages and limitations. Also, an empirical study of some existing land cover change detection algorithms and results have been presented in this paper.

A novel Variable Global Feature Selection Scheme (VGFSS) is proposed.VGFSS selects variable number of features from each class instead of equal features.The selection of features in VGFSS is based on distribution of terms in the classes.The methods are evaluated using Macro_F1 and Micro_F1 measure followed by Z-test.The VGFSS algorithm outperforms among seven competing methods in benchmark datasets. The feature selection is important to speed up the process of Automatic Text Document Classification (ATDC). At present, the most common method for discriminating feature selection is based on Global Filter-based Feature Selection Scheme (GFSS). The GFSS assigns a score to each feature based on its discriminating power and selects the top-N features from the feature set, where N is an empirically determined number. As a result, it may be possible that the features of a few classes are discarded either partially or completely. The Improved Global Feature Selection Scheme (IGFSS) solves this issue by selecting an equal number of representative features from all the classes. However, it suffers in dealing with an unbalanced dataset having large number of classes. The distribution of features in these classes are highly variable. In this case, if an equal number of features are chosen from each class, it may exclude some important features from the class containing a higher number of features. To overcome this problem, we propose a novel Variable Global Feature Selection Scheme (VGFSS) to select a variable number of features from each class based on the distribution of terms in the classes. It ensures that, a minimum number of terms are selected from each class. The numerical results on benchmark datasets show the effectiveness of the proposed algorithm VGFSS over classical information science methods and IGFSS.

Training speed of the classifier without degrading its predictive capability is an important concern in text classification. Feature selection plays a key role in this context. It selects a subset of most informative words (terms) from the set of all words. The correlative association of words towards the classes increases an incertitude for the words to represent a class. The representative words of a class are either of positive or negative nature. The standard feature selection methods, viz. Mutual Information (MI), Information Gain (IG), Discriminating Feature Selection (DFS) and Chi Square (CHI), do not consider positive and negative nature of the words that affects the performance of the classifiers. To address this issue, this paper presents a novel feature selection method named Correlative Association Score (CAS). It combines the strength, mutual information, and strong association of the words to determine their positive and negative nature for a class. CAS selects a few (k) informative words from the set of all words (m). These informative words generate a set of N-grams of length 1-3. Finally, the standard Apriori algorithm ensembles the power of CAS and CHI to select the top most, b informative N-grams, where b is a number set by an empirical evaluation. Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) classifiers evaluate the performance of the selected N-Grams. Four standard text data sets, viz. Webkb, 20Newsgroup, Ohsumed10, and Ohsumed23 are used for experimental analysis. Two standard performance measures named Macro_F1 and Micro_F1 show a significant improvement in the results using proposed CAS method.

Massive amount of Earth science data open an unprecedented opportunity to discover potentially valuable information. Earth science data are complex, nonlinear, high-dimensional data, and the sparsity of data in high-dimensional space poses major challenge in clustering of the data. Shared nearest neighbor clustering (SNN) algorithm is one of the well-known and efficient methods to handle high-dimensional spatiotemporal data. The SNN clustering method does not cluster all the data forming rigid boundary selection. This paper reports fuzzy shared nearest neighbor (FSNN) algorithm which is an enhancement of the SNN clustering method that has the capability of handling the data lying in the boundary regions by means of a fuzzy concept. The clusters obtained can be characterized by the cluster centroid, which summarizes the behavior of the ocean points in the cluster. The statistical measure is used to find the significant relation between the cluster centroids and the existing climate indices. In this study, correlation measure is used to find the significant pattern, such as teleconnection or dipole. The experimentation is performed on Indian continent latitude range $$7.5^{\circ }{-}37.5^{\circ }\hbox {N}$$ and longitude range $$67.5^{\circ }{-}97.5^{\circ }\hbox {E}$$ . Extensive experiments are carried out to compare the proposed approach with existing clustering methods such as K-means, fuzzy C-means and SNN. The proposed method, FSNN algorithm, not only handles the data lying in the overlapping region, but it also finds more compact and well-separated clusters. FSNN shows better results in terms of finding a significant correlation between cluster centroids and existing climate indices and validated by ground truth dataset.

Selection of relevant and appropriate features to characterize breast patterns is of paramount importance in breast tissue representation and classification in machine learning paradigm. Feature selection based on single evaluation criterion has shown limited capability in breast tumor detection and classification due to their biases towards single criterion. In this paper, a new hybrid feature selection scheme is used to determine most relevant features for classification of benign and malignant tumors in breast ultrasound images. The proposed approach uses ten different evaluation criteria to decide the relevance of a particular feature. The existing feature selection techniques are also reviewed. A new database of 178 breast ultrasound images consisting of 88 benign and 90 malignant cases are used in experiments. The performance of the proposed approach is compared with that of existing feature selection techniques using back-propagation artificial neural network (BPANN) and support vector machine (SVM) based classifiers. The results demonstrate that proposed feature selection approach outperformed traditional methods achieving significantly higher classification accuracy of 96.6% and 94.4% with BPANN and SVM classifiers respectively.

The chest radiograph is the globally accepted standard used for analysis of pulmonary diseases. This paper presents a method for automatic detection of pneumonia on segmented lungs using machine learning paradigm. The paper focuses on pixels in lungs segmented ROI (Region of Interest) that are more contributing toward pneumonia detection than the surrounding regions, thus the features of lungs segmented ROI confined area is extracted. The proposed method has been examined using five benchmarked classifiers named Multilayer Perceptron, Random forest, Sequential Minimal Optimization (SMO), Logistic Regression, and Classification via Regression. A dataset of a total of 412 chest X-ray images containing 206 normal and 206 pneumonic cases from the ChestX-ray14 dataset are used in experiments. The performance of the proposed method is compared with the traditional method using benchmarked classifiers. Experimental results demonstrate that the proposed method outperformed the existing method attaining a significantly higher accuracy of 95.63% with the Logistic Regression classifier and 95.39% with Multilayer Perceptron.

In this paper, we reviewed different forecasting models and proposed a neural network-based regression model for forecasting model price of rice. This study aims to prepare a neural network model that uses some of the existing data as feature and treat some data for defining target value. We also performed experimental evaluation of various forecasting models in real data of rice using time series data (price) of rice of India from 2001 to 2012. The algorithms are evaluated based on evaluation measures such as mean error (ME), root mean square error (MSE), mean absolute error (MAE), mean percentage error (MPE), mean absolute percentage error (MAPE) and mean absolute square error (MASE).

Highlights • Proposed automatic COVID screening (ACoS) system for detection of infected patients. • Random image augmentation is applied to incorporate the variability in the images. • Applied hierarchical (two phase) classification to segregate three classes. • Majority vote based classifier ensemble is used to combine model’s prediction. • Proposed method show promising potential to detect nCOVID-19 infected patients., Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.

New classification approach is developed.Proposed approach eliminates ambiguous and doubtful samples from training dataset.Proposed approach is used to classify breast tumors in ultrasound images.Proposed classifier outperforms conventional ones.Proposed method achieves high classification accuracy. The performance of supervised classification algorithms is highly dependent on the quality of training data. Ambiguous training patterns may misguide the classifier leading to poor classification performance. Further, the manual exploration of class labels is an expensive and time consuming process. An automatic method is needed to identify noisy samples in the training data to improve the decision making process. This article presents a new classification technique by combining an unsupervised learning technique (i.e. fuzzy c-means clustering (FCM)) and supervised learning technique (i.e. back-propagation artificial neural network (BPANN)) to categorize benign and malignant tumors in breast ultrasound images. Unsupervised learning is employed to identify ambiguous examples in the training data. Experiments were conducted on 178 B-mode breast ultrasound images containing 88 benign and 90 malignant cases on MATLABź software platform. A total of 457 features were extracted from ultrasound images followed by feature selection to determine the most significant features. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and Mathew's correlation coefficient (MCC) were used to access the performance of different classifiers. The result shows that the proposed approach achieves classification accuracy of 95.862% when all the 457 features were used for classification. However, the accuracy is reduced to 94.138% when only 19 most relevant features selected by multi-criterion feature selection approach were used for classification. The results were discussed in light of some recently reported studies. The empirical results suggest that eliminating doubtful training examples can improve the decision making performance of expert systems. The proposed approach show promising results and need further evaluation in other applications of expert and intelligent systems.

Due to the rapid growth of wireless communications and positioning technologies, trajectory data have become increasingly popular, posing great challenges to the researchers of data mining and machine learning community. Trajectory data are obtained using GPS devices that capture the position of an object at specific time intervals. These enormous amounts of data necessitates to explore efficient and effective techniques to extract useful information to solve real world problems. Traffic flow pattern mining is one of the challenging issues for many applications. In a literature significant number of approaches are available to cluster the trajectory data, however the clustering has not been explored for trajectories pattern mining in bi-directional road networks. This paper presents a novel technique for excavating heavy traffic flow patterns in bi-directional road network, i.e. identifying divisions of the roads where the traffic flow is very dense. The proposed technique works in two phases: phase I, finds the clusters of trajectory points based on density of trajectory points; phase II, arranges the clusters in sequence based on spatiotemporal values for each route and directions. These sequences represent the traffic flow patterns. All the routes and sections exceeding a user specified minimum traffic threshold are marked as high dense traffic areas. The experiments are performed on synthetic dataset. The proposed algorithm efficiently and accurately finds the dense traffic in bi-directional roads. Proposed clustering method is compared with the standard k-means clustering algorithm for the performance evaluation.

In this paper, we proposed a novel data mining approach Recursive Search Algorithm (RSA) to detect sudden changes in time series data-set. In literature, Modified Lunetta, cumulative sum (CUMSUM) MEAN, Yearly Delta, and Recursive Merging techniques are used to detect sudden changes in land covers using data mining approach. The main drawback of Modified Lunetta, CUMSUM MEAN, and Yearly Delta approach is that, it only identifies a time series is changed or not, while Recursive Merging technique finds the changed segment only. RSA approach has the capability to detect with high confidence to correctly compute the change point (time of change) in time series data, also detect the type of change (increase/decrease) occurred in series. The proposed algorithm is scalable, considerable improvement in performance in the presence of cyclic data. All experiments are performed on synthetic data-set, which is analogous to vegetation index time series data-set.

Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objective of this study is to mimic the expert radiologist’s interpretation procedure in computer-aided diagnosis (CAD) systems. We propose an automatic technique for detection of abnormal CXR images containing one or more pathologies like pleural effusion, infiltration, fibrosis, hila enlargement, dense consolidation, etc. due to tuberculosis (TB). The proposed abnormality detection technique is based on the hierarchical feature extraction scheme in which the features are used in two-level of hierarchy to categorize healthy and unhealthy groups. In level one the handcrafted geometrical features like shape, size, eccentricity, perimeter, etc. and in level 2 traditional first order statistical feature along with texture features like energy, entropy, contrast, correlation, etc. are extracted from segmented lung-fields. Further, a supervised classification approach is employed on the extracted features to detect normal and abnormal CXR images. The performance of the algorithm is validated on a total of 800 CXR images from two public datasets, namely the Montgomery set and Shenzhen set. The obtained results (accuracy = 95.60 ± 5.07% and area under curve (AUC) = 0.95 ± 0.06 for Montgomery collection, and accuracy = 99.40 ± 1.05% and AUC = 0.99 ± 0.01 for Shenzhen collection) shows the promising performance of the proposed technique for TB detection compared to the existing state of the art approaches. Further, the obtained results are statistically validated using Friedman post-hoc multiple comparison methods, which confirms the significance of the proposed method.

Radiography is one of the important clinical adjuncts for preliminary disease investigation. The X-ray images are corrupted with inherent quantum noise affecting the performance of computer-aided diagnosis systems. This paper presents an extensive experimental review and impact of six benchmark filters for reducing noise and disease classification on chest X-ray images. The tradeoff between de-noising and texture preserving performance is investigated through classification performances using the state-of-the-art machine learning methods – Support Vector Machine and Artificial Neural Network. Moreover, the qualitative, subjective, and statistical evaluation is performed by using the image quality metrics, expert radiologist opinion, and statistical test, respectively. The experimental results confirm the significant improvement in classification performance using Guided filtered images. Furthermore, the results of qualitative measures and subjective analysis demonstrate that the guided filter and anisotropic diffusion filter both performed significantly better. Finally, a non-parametric statistical test is used to validate statistical significance of the obtained results.