A hybrid convolutional neural network‐support vector machine architecture for classification of super‐resolution enhanced chromosome images

This article offers a framework of a hybrid convolution neural network (CNN) – support vector machine (SVM) model for the classification of human metaphase chromosome images. In this model, the features extracted by CNN were fed to the SVM classifier to label the chromosomes into 24 classes. Classification accuracy is generally affected by low‐resolution chromosome images. Hence, in the proposed work, a Laplacian pyramidal super‐resolution network (LaPSRN) was deployed to improve input chromosome images resolution before feeding them to the classifier network. The experimentation showed that due to LaPSRN, resolution‐enhanced images are obtained, which has improved the classification accuracy by 3% compared to the architecture without a super‐resolution block. In addition, instead of using the usual ReLU function in CNN architecture, a Swish activation function was utilized, which enhanced the validation accuracy by 0.8%. After experimenting with various hyper‐parameters for the CNN SVM architecture, the ADAM optimizer with step decay outperformed the other standard optimizers. The novel part of this work is the combination of Laplacian pyramidal super‐resolution with the hybrid CNN‐SVM model, which has not been employed yet for chromosome classification tasks as per our knowledge. The proposed hybrid CNN SVM architecture with the Swish activation function and super‐resolution techniques was tested on the metaphase chromosome images from the BioImLab dataset, and an improved classification accuracy of 94.6% was obtained. Moreover, the standard metrics for classification like F1 score, precision, support, and the confusion matrix values of the proposed CNN model with SVM classifier are superior to the prevailing CNN architectures for chromosome images.