The karyotype is analyzed to detect the genetic abnormalities. It is generated by arranging the chromosomes after extracting them from the metaphase chromosome images. The chromosomes are non-rigid bodies that contain the genetic information of an individual. The metaphase chromosome image spread contains the chromosomes, but these chromosomes are not distinct bodies; they can either be individual chromosomes or be touching one another; they may be bent or even may be overlapping and thus forming a cluster of chromosomes. The extraction of chromosomes from these touching and overlapping chromosomes is a very tedious process. The segmentation of a random metaphase chromosome image may not give us correct and accurate results. Therefore, before taking up a metaphase chromosome image for analysis, it must be analyzed for the orientation of the chromosomes it contains. The various reported methods for metaphase chromosome image selection for automatic karyotype generation are compared in this paper. After analysis, it has been concluded that each metaphase chromosome image selection method has its advantages and disadvantages.

Chromosomes are the genetic information carriers. Any modification to the structure or the number of chromosomes results in a medical condition termed as genetic defect. In order to uncover the genetic defects, the chromosomes are imaged during the cell division process. The images thus generated are termed as metaspread images and are used for identifying the genetic defects. It has been observed that the metaspread images generally suffer from intensity inhomogeneity and the chromosomes are also present in varied orientations, and as a result finding genetic defects from such images is a tedious process. Therefore, cytogeneticists manually select the images that can be used for the purpose of uncovering the genetic defects and the generation of the karyotype. In the proposed approach, a novel method is being presented using DenseNet architecture of the convolutional neural networks-based classifier, which classifies the human metaspread images into two distinct categories, namely, analyzable and non-analyzable based on the orientation of the chromosomes present in the metaspread images. This classification process will help to select the most prominent metaspread images for karyotype generation that has least amount of touching and overlapping chromosomes. The proposed method is novel in comparison to the earlier methods as it works on any type of image, be it G band images, MFISH images or the Q-banded images. The proposed method has been trained by using a ground truth of 156 750 metaspread images. The proposed classifier has been able to achieve an error rate of 1.46%.

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The genetic defects in the humans are uncovered by studying the chromosomes, as they are the genetic information carriers. They are non-rigid objects and they appear in different orientations when they are imaged. To find out the genetic defects, the chromosomes are pre-processed so that they are not touching, overlapping, and bent, and the noise is also discarded. The presence of bends, overlaps, or touches makes it difficult to uncover the genetic abnormalities. So there is a need for development of an efficient technique to classify the segmented chromosomes into different types and then pre-process them in order to correct their orientation. In this work, a hybrid classification technique based upon correlation-based feature selection and classification via regression approach, which will classify the segmented chromosomes into five categories viz; straight, overlapping, bent, touching, or noise is presented. The performance evaluation has been done using 1592 segmented chromosomes from Advance Digital Imaging Research data set. The over-all accuracy of 94.78 % has been obtained for the five class problem. The performance of the proposed classifier has been compared with Bayes Net, Naïve Bayes, Radial Bias Feed Forward Network, and k-nearest-neighbour classifiers. Based upon this categorization, different pre-processing techniques will be applied to correct the orientation of the chromosomes.

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A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.

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The chromosomes are the carriers of the geometric information, any alteration in the structure or number of these chromosomes is termed as genetic defect. These alterations cause malfunctioning in the proteins and are cause of the various underlying medical conditions that are hard to cure or detect by normal clinical procedures. In order to detect the underlying causes of these defects, the cells of the humans need to be imaged during the mitosis phase of cell division. During this phase, the chromosomes are the longest and can be easily studied and the alterations in the structure and count of the chromosomes can be analyzed easily. The chromosomes are non-rigid objects, due to which they appear in varied orientations, which makes them hard to be analyzed for the detection of structural defects. In order to detect the genetic abnormalities due to structural defects, the chromosomes need to be in straight orientation. Therefore, in this work, we propose to classify the segmented chromosomes from the metaspread images into straight, bent, touching overlapping or noise, so that the bent, touching, overlapping chromosomes can be preprocessed and straightened and the noisy objects be discarded. The classification has been done using a set of 17 different geometric features. We have proposed a Multilayer Perceptron-based classification approach to classify the chromosomes extracted from metaspread images into five distinct categories considering their orientation. The results of the classification have been analyzed using the segmented objects of the Advance Digital Imaging Research (ADIR) dataset. The proposed technique is capable of classifying the segmented chromosomes with 94.28% accuracy. The performance of the proposed technique has been compared with seven other state-of-the-art classifiers and superior results have been achieved by the proposed method.