Classifying gene expression data into clinically relevant categories poses unique challenges, particularly due to the high dimensionality, computational complexity, and class imbalance of the datasets. Traditional machine learning algorithms, such as decision trees, support vector machines, and gradient boosting, often struggle to capture the intricate nonlinear relationships inherent in such data. This study introduces an innovative methodology, Enhanced Tabular Convolution (Enhanced TAC), to address these challenges by transforming tabular RNA-Seq gene expression data into image representations and leveraging convolutional neural networks (CNNs) for classification. Key steps include feature extraction, image transformation, histogram equalization for enhancement, and fine-tuning a pre-trained ResNet34 model for accurate predictions. The promising experimental results suggest that our methodology could significantly improve patient diagnosis, encouraging its adoption within the medical community.