A Survey on Automation of Chromosome Based Genetic Diagnosis Using Machine Learning

Genetic Diagnosis based on chromosomes is the detection of specific chromosomes that changes the human behaviors in terms of abnormalities leading to several chromosomal disorders. The most regular approach by specialist in medical field is Karyotyping. The procedure is to pair the Chromosomes in order and generate a Listing, this process is called Karyotyping and the resulted image is called Karyogram. Then the Specialists analyze image based on the shape, size and relationship of the different image segments, as a result Manual intervention is a Time-Consuming process. This Paper provides a broad review of past and present research in the evolution of different supervised methods used for genetic diagnosis of Karyograms. The prime focus is to understand about abnormality detection and providing the confidence of result in Chromosome domain of various supervised methods adopted. We aim to know how the images are divided to normal and abnormal categories and increasing the confidentiality level which results in a review of an empirical study of chromosome and karyotyping, appropriate data preprocessing, experiments on different neural network systems along with conditions and comparison of them.