AI at the Frontier of Cytogenetics: Chromosome Structure Detection
Keywords:
chromosomal abnormalities, deep learning, CNN, ChromoNet, genetic disorders, diagnosis, image analysis.Abstract
Chromosomal abnormalities are the main cause of many genetic disorders; hence, proper diagnosis will be vital in providing adequate treatment. The paper introduces a CNN called ChromoNet designed to classify whether chromosomal structures are normal or abnormal. ChromoNet uses the advanced technique that has convolutional techniques such as ReLU optimization, max pooling, and batch normalization for improving feature extraction and preventing overfitting. It was trained on an extremely large image set of chromosomes with great promise in producing a desirable classification accuracy. It shows how chromosome image analysis is one of the main capabilities that may help to develop genetic research and improve diagnosis for chromosomal abnormalities.
References
Zhang, Wenbo, et al(2018) . "Chromosome classification with convolutional neural network based deep learning." 2018 11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). https://ieeexplore.ieee.org/abstract/document/863322 8/
Al-Kharrazi, Mona Salem, Lamiaa A. Alrifai, and Mai Ahmed Fadel. "system for chromosome karyotyping to recognize the most common numerical deep learning." https://ieeexplore.ieee.org/abstract/document/917872 1/
Nimitha, N., P. Ezhumalai, and Arun Chokkalingam. "An improved deep convolutional neural network architecture for chromosome detection abnormality using hybrid optimization model." https://analyticalsciencejournals.onlinelibrary.wiley.c om/doi/abs/10.1002/jemt.24170
