International Journal of Computer and Applications

Clustering is an unsupervised learning technique which aims at grouping a set of objects into clusters so that objects in the same clusters should be similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in other clusters. Cluster analysis aims to group a collection of patterns into clusters based on similarity. A typical clustering technique uses a similarity function for comparing various data items. This paper covers the survey of various clustering techniques, the current similarity measures based on distance based clustering, explains the limitations associated with the existing clustering techniques and propose that the combination of the advantages of the existing systems can help overcome the limitations of the existing systems. General Terms Data Mining, Machine Learning, Clustering, Pattern based Similarity, Negative Data, et. al.

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Clustering Techniques and the Similarity Measures used in Clustering: A Survey

Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally In this paper, studies conducted with the aid of DL networks to distinguish ASD were investigated Rehabilitation tools provided by supporting ASD patients utilizing DL networks were also assessed Finally, we presented important challenges in this automated detection and rehabilitation of ASD

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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Brain tumor detection and classification using machine learning: a comprehensive survey

Research Methodology : A Practical and Scientific Approach

Since the introduction of the automated karyotyping systems, segmentation and classification of touching and overlapping chromosomes in the metaphase images are major challenges. The earlier reported techniques for disentangling the chromosome overlaps have limited success and use only color information in case of multispectral imaging. Most of them are restricted to separation of single overlap of two chromosomes. This paper introduces a novel algorithm to extricate overlapping chromosomes in a metaphase image. The proposed technique uses Delaunay triangulation to automatically identify the number of overlaps in a cluster followed by the detection of the appropriate cut-points. The banding information on the overlapped region further resolves the set of overlapping chromosomes with the identified cut-points. The proposed algorithm has been tested with four data sets of 60 overlapping cases, obtained from publically available databases and private genetic labs. The experimental results provide an overall accuracy of 75–100 % for resolving the cluster of 1–6 overlaps.

A novel approach for efficient extrication of overlapping chromosomes in automated karyotyping

Automated karyotyping for chromosome classification is an essential task in cytogenetics for diagnosis of genetic disorders and has therefore been an important pattern recognition problem. The existing learning approaches generally discard the previously acquired knowledge and often require retraining, leading to space and time complexities. Incremental learning methods have gained popularity in the current learning scenarios to deal with these issues. This study proposes a novel approach of incremental learning for chromosomes classification for automated karyotyping of metaphase chromosomes. It addresses the issue of catastrophic forgetting with the generation of new class and performs knowledge amassing to classify the chromosomes in Denver groups (A–G). The adaptive nature of the proposed method contributes to its sustained accuracy even for dynamically changing data. An average classification accuracy of 97% is achieved with experimentation on 1800 chromosomes from a publicly available database.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2012.0160

Efficient karyotyping of metaphase chromosomes using incremental learning

Since the birth of the automated karyotyping systems by the aid of computers, building a fully automated chromosome analysis system has been an ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been a major challenge especially due to overlapping and touching chromosomes. The earlier reported methods have limited success as they are sensitive to scale variations, computationally complex, use only color information in case of multispectral imaging and had challenges in segmentation. The proposed technique addresses the challenge of separating the touching chromosomes using initially the modified snake algorithm to disentangle the cluster of touching chromosomes from the metaphase image and then a greedy approach based on combinatorial computational geometry of the pixels on the boundary of the cluster is used to identify and resolve the set of touching chromosomes. Contribution and novelty of this work lies in the ability of the algorithm to successfully separate the clusters of any number of touching chromosomes. System performance was tested and analyzed using a variety of metaphase images exhibiting various levels of touching chromosomes giving an overall accuracy of 100 % for resolving the cluster with 2 touching chromosomes and 95 % for separating a cluster of 3, 4 touches. The overall time was 2.4 seconds.

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Automated Karyotyping of Metaphase Cells with Touching Chromosomes

Autism spectrum disorder is a brain disorder in children and is detected using magnetic resonance imaging scan and electroencephalography (EEG) signals. This paper presents a system to detect autis...

Mousami V. Munot

Papers

Research Methodology : A Practical and Scientific Approach

A novel approach for efficient extrication of overlapping chromosomes in automated karyotyping

Efficient karyotyping of metaphase chromosomes using incremental learning

Automated Karyotyping of Metaphase Cells with Touching Chromosomes

An Efficient Approach for Detection of Autism Spectrum Disorder Using Electroencephalography Signal