Deep learning models such as CNNs have surpassed human performance in computer vision tasks such as image classi- fication. However, despite their sophistication, these models lack interpretability which can lead to biased outcomes re- flecting existing prejudices in the data. We aim to make pre- dictions made by a CNN interpretable. Hence, we present a novel framework called NeSyFOLD to create a neurosym- bolic (NeSy) model for image classification tasks. The model is a CNN with all layers following the last convolutional layer replaced by a stratified answer set program (ASP) derived from the last layer kernels. The answer set program can be viewed as a rule-set, wherein the truth value of each pred- icate depends on the activation of the corresponding kernel in the CNN. The rule-set serves as a global explanation for the model and is interpretable. We also use our NeSyFOLD framework with a CNN that is trained using a sparse kernel learning technique called Elite BackProp (EBP). This leads to a significant reduction in rule-set size without compromising accuracy or fidelity thus improving scalability of the NeSy model and interpretability of its rule-set. Evaluation is done on datasets with varied complexity and sizes. We also pro- pose a novel algorithm for labelling the predicates in the rule- set with meaningful semantic concept(s) learnt by the CNN. We evaluate the performance of our “semantic labelling algo- rithm” to quantify the efficacy of the semantic labelling for both the NeSy model and the NeSy-EBP model.