Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome images - Menaka, D.; Vaidyanathan, S. Ganesh (Multidimensional systems and signal processing, 2022)
Chromosome karyotyping is pivotal in the diagnosis of many genetic disorders and birth defects. Manual karyotyping is a tedious procedure and various techniques were proposed in the literature to automate this process. As Artificial Intelligence algorithms are unfolding their vast potential in a plethora of applications, automatic karyotyping of chromosomes remains one of the critical challenges yet to be fully addressed. Deep learning techniques have gained rapid momentum in the computer vision domain specifically for chromosome image segmentation and classification. This paper proposes Chromenet—a simplified CNN architecture for classification of metaphase Q banded chromosome images and achieves good classification accuracy than the state of art methods proposed in recent times. This research discusses the comparison of newly suggested optimizers with the standard deep learning optimizers. Different optimizers like SGD, ADAM, Adagrad, Nadam, Adabound and RMSprop were used and a comparison of classification accuracy and loss was obtained. In this work, it was proposed to use step-based decay for ADAM optimizer in which the learning rate is modified every 15–20 epochs. After excessive experimentation, it was found that the ADAM optimizer with step-based decay achieved classification accuracy around 92.6 % which is better in comparison to the other adaptive optimizers for the proposed CNN architecture. Moreover, the performance metrics like F1 score, precision, support, sensitivity, Negative predictive value, Jaccard score and confusion matrix used for the evaluation of the proposed model prove that the proffered architecture is more suitable for classification of human chromosome images when compared to the other methods proposed in the recent literature.