Convolutional Neural Networks (CNNs) have shown remarkable performance in image processing tasks, including image segmentation and feature extraction. However, training CNNs can be challenging when dealing with uneven data distribution, leading to biased predictions and overall poor performance. To address this issue, this research proposes an "Enhanced CNN" algorithm that incorporates a method called "Balanced Batch Normalization."