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Recent developments in satellite image processing tend to eliminate the need for intensive on-site surveys of urban or rural areas for infrastructure allocation planning. In particular, the detection of buildings in satellite images can significantly aid in rural or urban planning. However, detecting individual buildings in low-resolution satellite images is challenging due to a lack of visual clarity. In order to address this problem, we propose a computer vision-based hybrid framework to detect densely constructed building regions in low resolution satellite images, which can also serve as an assistive framework for automated geo-spatial survey of various sites. Our hybrid framework is comprised of three modules, namely the Mask R-CNN module, an ANN-based refinement module, and a DBSCAN based refinement module. The Mask R-CNN is employed to predict probable clustered building regions in the satellite image, whereas the ANN-based refinement module is applied to remove homogeneous regions (e.g. vegetation area) from the Mask R-CNN-detected clustered building region. Finally, a DBSCAN-based refinement module removes non-congested built-up regions so that densely constructed built-up areas can be identified with better precision. Our experimentation using publicly accessible satellite image datasets (AIRS dataset-Kaggle) establishes the effectiveness of the proposed hybrid framework in identifying densely populated building areas in low-resolution satellite images. The outcomes of the proposed framework demonstrated an approximately 5–10% improvement over various Mask R-CNN and U-Net-based approaches in terms of achieving better precision value of the detection. [ABSTRACT FROM AUTHOR]

This article presents a comparative study on the outcomes of secondary peritonitis in different regions of India using the Mannheim peritonitis index (MPI). The study found that factors such as age, gender, and site of perforation were correlated with mortality rates. The study suggests that the MPI scoring system is effective in predicting risk and recommends aggressive resuscitation and intensive care support for patients with a high MPI score. The article also discusses the prevalence of upper gastrointestinal tract perforations in the Indian population and the importance of early presentation and proper management. [Extracted from the article]

Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., Jaccard & Dice co-efficient, relative error, segmentation accuracy, error and Fowlkes–Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62%, 4.38% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results. [ABSTRACT FROM AUTHOR]

This article discusses a rare case of the Miller Fisher variant of Guillain-Barré Syndrome (GBS) occurring after a modified radical mastectomy in a breast cancer patient. The patient experienced weakness and difficulty in eye movements, which worsened over time. The diagnosis was confirmed through clinical presentation, cerebrospinal fluid analysis, and nerve conduction studies. Due to financial constraints, the patient did not receive treatment but showed spontaneous recovery after several weeks. The case highlights the importance of recognizing neurological complications in postoperative patients, particularly those with underlying cancer. [Extracted from the article]

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and F1-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models. [ABSTRACT FROM AUTHOR]

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from (3 × 3) to (5 × 5) or (7 × 7) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images. [ABSTRACT FROM AUTHOR]

Cancer prediction from gene expression data is a very challenging area of research in the field of computational biology and bioinformatics. Conventional classifiers are often unable to achieve desired accuracy due to the lack of 'sufficient' training patterns in terms of clinically labeled samples. Active learning technique, in this respect, can be useful as it automatically finds only few most informative (or confusing) samples to get their class labels from the experts and those are added to the training set, which can improve the accuracy of the prediction consequently. A novel active learning technique using fuzzy-rough nearest neighbor classifier (ALFRNN) is proposed in this paper for cancer classification from microarray gene expression data. The proposed ALFRNN method is capable of dealing with the uncertainty, overlapping and indiscernibility often present in cancer subtypes (classes) of the gene expression data. The performance of the proposed method is tested using different real-life microarray gene expression cancer datasets and its performance is compared with five other state-of-the-art techniques (out of which three are active learning-based and two are traditional classification methods) in terms of percentage accuracy, precision, recall, F 1 -measures and kappa. Superiority of the proposed method over the other counterpart algorithms is established from experimental results for cancer prediction and results of the paired t -test confirm statistical significance of the results in favor of the proposed method for almost all the datasets. [ABSTRACT FROM AUTHOR]

The study of ant colonies behavior and their self-organizing capabilities is of interest to machine learning community, because it provides models of distributed adaptive organization which are useful to solve difficult optimization and classification problems among others. Social insects like ants, bees deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone, that causes clumping behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This article presents a new algorithm (called, APC) for pattern classification based on this property of aggregation pheromone found in natural behavior of real ants. Here each data pattern is considered as an ant, and the training patterns (ants) form several groups or colonies depending on the number of classes present in the data set. A new test pattern (ant) will move along the direction where average aggregation pheromone density (at the location of the new ant) formed due to each colony of ants is higher and hence eventually it will join that colony. Thus each individual test pattern (ant) will finally join a particular colony. The proposed algorithm is evaluated with a number of benchmark data sets as well as various kinds of artificially generated data sets using three evaluationmeasures. Results are compared with four other well known conventional classification techniques. Experimental results show the potentiality of the proposed algorithm in terms of all the evaluation measures compared to other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This paper presents a novel method for image segmentation considering the aggregation behavior of ants. Image segmentation is viewed as a clustering problem which aims to partition a given set of pixels into a number of homogenous clusters/segments. At each location of a data point, representing a pixel, an ant is placed; and the ants are allowed to move in the search space to find out positions with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different positions of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of different types of images using various cluster validity measures. Results are compared with those obtained using k-means and mean shift clustering algorithms and are found to be superior. [Copyright &y& Elsevier]